Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population–Based Study Article Swipe
Thien Vu
,
Yoshihiro Kokubo
,
Mai Inoue
,
Masaki Yamamoto
,
Attayeb Mohsen
,
Agustin Martin‐Morales
,
Research Dawadi
,
Takao Inoué
,
J. Tay
,
Mari Yoshizaki
,
Naoki Watanabe
,
Yuki Kuriya
,
Chisa Matsumoto
,
Ahmed Arafa
,
Yoko M. Nakao
,
Yuka Kato
,
Masayuki Teramoto
,
Michihiro Araki
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.2196/68066
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.2196/68066
Background Coronary heart disease (CHD) is a major cause of morbidity and mortality worldwide. Identifying key risk factors is essential for effective risk assessment and prevention. A data-driven approach using machine learning (ML) offers advanced techniques to analyze complex, nonlinear, and high-dimensional datasets, uncovering novel predictors of CHD that go beyond the limitations of traditional models, which rely on predefined variables. Objective This study aims to evaluate the contribution of various risk factors to CHD, focusing on both established and novel markers using ML techniques. Methods The study recruited 7672 participants aged 30-84 years from Suita City, Japan, between 1989 and 1999. Over an average of 15 years, participants were monitored for cardiovascular events. A total of 7260 participants and 28 variables were included in the analysis after excluding individuals with missing outcome data and eliminating unnecessary variables. Five ML models—logistic regression, random forest (RF), support vector machine, Extreme Gradient Boosting, and Light Gradient-Boosting Machine—were applied for predicting CHD incidence. Model performance was evaluated using accuracy, sensitivity, specificity, precision, area under the curve, F 1 -score, calibration curves, observed-to-expected ratios, and decision curve analysis. Additionally, Shapley Additive Explanations (SHAPs) were used to interpret the prediction models and understand the contribution of various risk factors to CHD. Results Among 7260 participants, 305 (4.2%) were diagnosed with CHD. The RF model demonstrated the highest performance, with an accuracy of 0.73 (95% CI 0.64‐0.80), sensitivity of 0.74 (95% CI 0.62‐0.84), specificity of 0.72 (95% CI 0.61‐0.83), and an area under the curve of 0.73 (95% CI 0.65‐0.80). RF also showed excellent calibration, with predicted probabilities closely aligning with observed outcomes, and provided substantial net benefit across a range of risk thresholds, as demonstrated by decision curve analysis. SHAP analysis elucidated key predictors of CHD, including the intima-media thickness (IMT_cMax) of the common carotid artery, blood pressure, lipid profiles (non–high-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides), and estimated glomerular filtration rate. Novel risk factors identified as significant contributors to CHD risk included lower calcium levels, elevated white blood cell counts, and body fat percentage. Furthermore, a protective effect was observed in women, suggesting the potential necessity for gender-specific risk assessment strategies in future cardiovascular health evaluations. Conclusions We developed a model to predict CHD using ML and applied SHAP methods for interpretation. This approach highlights the multifactor nature of CHD risk evaluation, aiming to support health care professionals in identifying risk factors and formulating effective prevention strategies.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2196/68066
- OA Status
- gold
- Cited By
- 10
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407872939
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4407872939Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.2196/68066Digital Object Identifier
- Title
-
Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population–Based StudyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-02-24Full publication date if available
- Authors
-
Thien Vu, Yoshihiro Kokubo, Mai Inoue, Masaki Yamamoto, Attayeb Mohsen, Agustin Martin‐Morales, Research Dawadi, Takao Inoué, J. Tay, Mari Yoshizaki, Naoki Watanabe, Yuki Kuriya, Chisa Matsumoto, Ahmed Arafa, Yoko M. Nakao, Yuka Kato, Masayuki Teramoto, Michihiro ArakiList of authors in order
- Landing page
-
https://doi.org/10.2196/68066Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.2196/68066Direct OA link when available
- Concepts
-
Preprint, Medicine, Computer science, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 10Per-year citation counts (last 5 years)
- References (count)
-
53Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4407872939 |
|---|---|
| doi | https://doi.org/10.2196/68066 |
| ids.doi | https://doi.org/10.2196/68066 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/40354648 |
| ids.openalex | https://openalex.org/W4407872939 |
| fwci | 75.2990842 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D006801 |
| mesh[0].is_major_topic | False |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Humans |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D008875 |
| mesh[1].is_major_topic | False |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Middle Aged |
| mesh[2].qualifier_ui | |
| mesh[2].descriptor_ui | D005260 |
| mesh[2].is_major_topic | False |
| mesh[2].qualifier_name | |
| mesh[2].descriptor_name | Female |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D008297 |
| mesh[3].is_major_topic | False |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Male |
| mesh[4].qualifier_ui | Q000453 |
| mesh[4].descriptor_ui | D007564 |
| mesh[4].is_major_topic | False |
| mesh[4].qualifier_name | epidemiology |
| mesh[4].descriptor_name | Japan |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D000368 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Aged |
| mesh[6].qualifier_ui | Q000379 |
| mesh[6].descriptor_ui | D018570 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | methods |
| mesh[6].descriptor_name | Risk Assessment |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D000069550 |
| mesh[7].is_major_topic | True |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Machine Learning |
| mesh[8].qualifier_ui | Q000453 |
| mesh[8].descriptor_ui | D003327 |
| mesh[8].is_major_topic | True |
| mesh[8].qualifier_name | epidemiology |
| mesh[8].descriptor_name | Coronary Disease |
| mesh[9].qualifier_ui | Q000175 |
| mesh[9].descriptor_ui | D003327 |
| mesh[9].is_major_topic | True |
| mesh[9].qualifier_name | diagnosis |
| mesh[9].descriptor_name | Coronary Disease |
| mesh[10].qualifier_ui | |
| mesh[10].descriptor_ui | D000328 |
| mesh[10].is_major_topic | False |
| mesh[10].qualifier_name | |
| mesh[10].descriptor_name | Adult |
| mesh[11].qualifier_ui | |
| mesh[11].descriptor_ui | D000369 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | |
| mesh[11].descriptor_name | Aged, 80 and over |
| mesh[12].qualifier_ui | |
| mesh[12].descriptor_ui | D012307 |
| mesh[12].is_major_topic | False |
| mesh[12].qualifier_name | |
| mesh[12].descriptor_name | Risk Factors |
| mesh[13].qualifier_ui | |
| mesh[13].descriptor_ui | D015994 |
| mesh[13].is_major_topic | False |
| mesh[13].qualifier_name | |
| mesh[13].descriptor_name | Incidence |
| mesh[14].qualifier_ui | |
| mesh[14].descriptor_ui | D000082742 |
| mesh[14].is_major_topic | False |
| mesh[14].qualifier_name | |
| mesh[14].descriptor_name | Heart Disease Risk Factors |
| mesh[15].qualifier_ui | |
| mesh[15].descriptor_ui | D000095225 |
| mesh[15].is_major_topic | False |
| mesh[15].qualifier_name | |
| mesh[15].descriptor_name | East Asian People |
| mesh[16].qualifier_ui | |
| mesh[16].descriptor_ui | D006801 |
| mesh[16].is_major_topic | False |
| mesh[16].qualifier_name | |
| mesh[16].descriptor_name | Humans |
| mesh[17].qualifier_ui | |
| mesh[17].descriptor_ui | D008875 |
| mesh[17].is_major_topic | False |
| mesh[17].qualifier_name | |
| mesh[17].descriptor_name | Middle Aged |
| mesh[18].qualifier_ui | |
| mesh[18].descriptor_ui | D005260 |
| mesh[18].is_major_topic | False |
| mesh[18].qualifier_name | |
| mesh[18].descriptor_name | Female |
| mesh[19].qualifier_ui | |
| mesh[19].descriptor_ui | D008297 |
| mesh[19].is_major_topic | False |
| mesh[19].qualifier_name | |
| mesh[19].descriptor_name | Male |
| mesh[20].qualifier_ui | Q000453 |
| mesh[20].descriptor_ui | D007564 |
| mesh[20].is_major_topic | False |
| mesh[20].qualifier_name | epidemiology |
| mesh[20].descriptor_name | Japan |
| mesh[21].qualifier_ui | |
| mesh[21].descriptor_ui | D000368 |
| mesh[21].is_major_topic | False |
| mesh[21].qualifier_name | |
| mesh[21].descriptor_name | Aged |
| mesh[22].qualifier_ui | Q000379 |
| mesh[22].descriptor_ui | D018570 |
| mesh[22].is_major_topic | False |
| mesh[22].qualifier_name | methods |
| mesh[22].descriptor_name | Risk Assessment |
| mesh[23].qualifier_ui | |
| mesh[23].descriptor_ui | D000069550 |
| mesh[23].is_major_topic | True |
| mesh[23].qualifier_name | |
| mesh[23].descriptor_name | Machine Learning |
| mesh[24].qualifier_ui | Q000453 |
| mesh[24].descriptor_ui | D003327 |
| mesh[24].is_major_topic | True |
| mesh[24].qualifier_name | epidemiology |
| mesh[24].descriptor_name | Coronary Disease |
| mesh[25].qualifier_ui | Q000175 |
| mesh[25].descriptor_ui | D003327 |
| mesh[25].is_major_topic | True |
| mesh[25].qualifier_name | diagnosis |
| mesh[25].descriptor_name | Coronary Disease |
| mesh[26].qualifier_ui | |
| mesh[26].descriptor_ui | D000328 |
| mesh[26].is_major_topic | False |
| mesh[26].qualifier_name | |
| mesh[26].descriptor_name | Adult |
| mesh[27].qualifier_ui | |
| mesh[27].descriptor_ui | D000369 |
| mesh[27].is_major_topic | False |
| mesh[27].qualifier_name | |
| mesh[27].descriptor_name | Aged, 80 and over |
| mesh[28].qualifier_ui | |
| mesh[28].descriptor_ui | D012307 |
| mesh[28].is_major_topic | False |
| mesh[28].qualifier_name | |
| mesh[28].descriptor_name | Risk Factors |
| mesh[29].qualifier_ui | |
| mesh[29].descriptor_ui | D015994 |
| mesh[29].is_major_topic | False |
| mesh[29].qualifier_name | |
| mesh[29].descriptor_name | Incidence |
| mesh[30].qualifier_ui | |
| mesh[30].descriptor_ui | D000082742 |
| mesh[30].is_major_topic | False |
| mesh[30].qualifier_name | |
| mesh[30].descriptor_name | Heart Disease Risk Factors |
| mesh[31].qualifier_ui | |
| mesh[31].descriptor_ui | D000095225 |
| mesh[31].is_major_topic | False |
| mesh[31].qualifier_name | |
| mesh[31].descriptor_name | East Asian People |
| mesh[32].qualifier_ui | |
| mesh[32].descriptor_ui | D006801 |
| mesh[32].is_major_topic | False |
| mesh[32].qualifier_name | |
| mesh[32].descriptor_name | Humans |
| mesh[33].qualifier_ui | |
| mesh[33].descriptor_ui | D008875 |
| mesh[33].is_major_topic | False |
| mesh[33].qualifier_name | |
| mesh[33].descriptor_name | Middle Aged |
| mesh[34].qualifier_ui | |
| mesh[34].descriptor_ui | D005260 |
| mesh[34].is_major_topic | False |
| mesh[34].qualifier_name | |
| mesh[34].descriptor_name | Female |
| mesh[35].qualifier_ui | |
| mesh[35].descriptor_ui | D008297 |
| mesh[35].is_major_topic | False |
| mesh[35].qualifier_name | |
| mesh[35].descriptor_name | Male |
| mesh[36].qualifier_ui | Q000453 |
| mesh[36].descriptor_ui | D007564 |
| mesh[36].is_major_topic | False |
| mesh[36].qualifier_name | epidemiology |
| mesh[36].descriptor_name | Japan |
| mesh[37].qualifier_ui | |
| mesh[37].descriptor_ui | D000368 |
| mesh[37].is_major_topic | False |
| mesh[37].qualifier_name | |
| mesh[37].descriptor_name | Aged |
| mesh[38].qualifier_ui | Q000379 |
| mesh[38].descriptor_ui | D018570 |
| mesh[38].is_major_topic | False |
| mesh[38].qualifier_name | methods |
| mesh[38].descriptor_name | Risk Assessment |
| mesh[39].qualifier_ui | |
| mesh[39].descriptor_ui | D000069550 |
| mesh[39].is_major_topic | True |
| mesh[39].qualifier_name | |
| mesh[39].descriptor_name | Machine Learning |
| mesh[40].qualifier_ui | Q000453 |
| mesh[40].descriptor_ui | D003327 |
| mesh[40].is_major_topic | True |
| mesh[40].qualifier_name | epidemiology |
| mesh[40].descriptor_name | Coronary Disease |
| mesh[41].qualifier_ui | Q000175 |
| mesh[41].descriptor_ui | D003327 |
| mesh[41].is_major_topic | True |
| mesh[41].qualifier_name | diagnosis |
| mesh[41].descriptor_name | Coronary Disease |
| mesh[42].qualifier_ui | |
| mesh[42].descriptor_ui | D000328 |
| mesh[42].is_major_topic | False |
| mesh[42].qualifier_name | |
| mesh[42].descriptor_name | Adult |
| mesh[43].qualifier_ui | |
| mesh[43].descriptor_ui | D000369 |
| mesh[43].is_major_topic | False |
| mesh[43].qualifier_name | |
| mesh[43].descriptor_name | Aged, 80 and over |
| mesh[44].qualifier_ui | |
| mesh[44].descriptor_ui | D012307 |
| mesh[44].is_major_topic | False |
| mesh[44].qualifier_name | |
| mesh[44].descriptor_name | Risk Factors |
| mesh[45].qualifier_ui | |
| mesh[45].descriptor_ui | D015994 |
| mesh[45].is_major_topic | False |
| mesh[45].qualifier_name | |
| mesh[45].descriptor_name | Incidence |
| mesh[46].qualifier_ui | |
| mesh[46].descriptor_ui | D000082742 |
| mesh[46].is_major_topic | False |
| mesh[46].qualifier_name | |
| mesh[46].descriptor_name | Heart Disease Risk Factors |
| mesh[47].qualifier_ui | |
| mesh[47].descriptor_ui | D000095225 |
| mesh[47].is_major_topic | False |
| mesh[47].qualifier_name | |
| mesh[47].descriptor_name | East Asian People |
| mesh[48].qualifier_ui | |
| mesh[48].descriptor_ui | D006801 |
| mesh[48].is_major_topic | False |
| mesh[48].qualifier_name | |
| mesh[48].descriptor_name | Humans |
| mesh[49].qualifier_ui | |
| mesh[49].descriptor_ui | D008875 |
| mesh[49].is_major_topic | False |
| mesh[49].qualifier_name | |
| mesh[49].descriptor_name | Middle Aged |
| type | article |
| title | Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population–Based Study |
| biblio.issue | |
| biblio.volume | 9 |
| biblio.last_page | e68066 |
| biblio.first_page | e68066 |
| topics[0].id | https://openalex.org/T11396 |
| topics[0].field.id | https://openalex.org/fields/36 |
| topics[0].field.display_name | Health Professions |
| topics[0].score | 0.9973999857902527 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/3605 |
| topics[0].subfield.display_name | Health Information Management |
| topics[0].display_name | Artificial Intelligence in Healthcare |
| topics[1].id | https://openalex.org/T13702 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9488000273704529 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Machine Learning in Healthcare |
| is_xpac | False |
| apc_list.value | 1500 |
| apc_list.currency | USD |
| apc_list.value_usd | 1500 |
| apc_paid.value | 1500 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 1500 |
| concepts[0].id | https://openalex.org/C43169469 |
| concepts[0].level | 2 |
| concepts[0].score | 0.9073318243026733 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q580922 |
| concepts[0].display_name | Preprint |
| concepts[1].id | https://openalex.org/C71924100 |
| concepts[1].level | 0 |
| concepts[1].score | 0.36240842938423157 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[1].display_name | Medicine |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.32411128282546997 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C136764020 |
| concepts[3].level | 1 |
| concepts[3].score | 0.12955236434936523 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q466 |
| concepts[3].display_name | World Wide Web |
| keywords[0].id | https://openalex.org/keywords/preprint |
| keywords[0].score | 0.9073318243026733 |
| keywords[0].display_name | Preprint |
| keywords[1].id | https://openalex.org/keywords/medicine |
| keywords[1].score | 0.36240842938423157 |
| keywords[1].display_name | Medicine |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.32411128282546997 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/world-wide-web |
| keywords[3].score | 0.12955236434936523 |
| keywords[3].display_name | World Wide Web |
| language | en |
| locations[0].id | doi:10.2196/68066 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210231566 |
| locations[0].source.issn | 2561-1011 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2561-1011 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | JMIR Cardio |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | JMIR Cardio |
| locations[0].landing_page_url | https://doi.org/10.2196/68066 |
| locations[1].id | pmid:40354648 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | JMIR cardio |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/40354648 |
| locations[2].id | pmh:oai:doaj.org/article:54df5baacd0c459d9bbad8dad8c41da3 |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | JMIR Cardio, Vol 9, Pp e68066-e68066 (2025) |
| locations[2].landing_page_url | https://doaj.org/article/54df5baacd0c459d9bbad8dad8c41da3 |
| locations[3].id | pmh:oai:pubmedcentral.nih.gov:12088616 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S2764455111 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | PubMed Central |
| locations[3].source.host_organization | https://openalex.org/I1299303238 |
| locations[3].source.host_organization_name | National Institutes of Health |
| locations[3].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[3].license | other-oa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/other-oa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | JMIR Cardio |
| locations[3].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/12088616 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5088690923 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-6956-0191 |
| authorships[0].author.display_name | Thien Vu |
| authorships[0].countries | JP, VN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I2801941164 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Cardiac Surgery, Cardiovascular Center, Cho Ray Hospital, Ho Chi Minh City, Vietnam. |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I160046202 |
| authorships[0].affiliations[1].raw_affiliation_string | NCD Epidemiology Research Center, Shiga University of Medical Science, Shiga, Otsu, Japan. |
| authorships[0].affiliations[2].institution_ids | https://openalex.org/I4210086383 |
| authorships[0].affiliations[2].raw_affiliation_string | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457. |
| authorships[0].institutions[0].id | https://openalex.org/I4210086383 |
| authorships[0].institutions[0].ror | https://ror.org/001rkbe13 |
| authorships[0].institutions[0].type | facility |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210086383 |
| authorships[0].institutions[0].country_code | JP |
| authorships[0].institutions[0].display_name | National Institute of Biomedical Innovation, Health and Nutrition |
| authorships[0].institutions[1].id | https://openalex.org/I160046202 |
| authorships[0].institutions[1].ror | https://ror.org/00d8gp927 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I160046202 |
| authorships[0].institutions[1].country_code | JP |
| authorships[0].institutions[1].display_name | Shiga University of Medical Science |
| authorships[0].institutions[2].id | https://openalex.org/I2801941164 |
| authorships[0].institutions[2].ror | https://ror.org/00n8yb347 |
| authorships[0].institutions[2].type | healthcare |
| authorships[0].institutions[2].lineage | https://openalex.org/I2801941164 |
| authorships[0].institutions[2].country_code | VN |
| authorships[0].institutions[2].display_name | Cho Ray Hospital |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Thien Vu |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457., Department of Cardiac Surgery, Cardiovascular Center, Cho Ray Hospital, Ho Chi Minh City, Vietnam., NCD Epidemiology Research Center, Shiga University of Medical Science, Shiga, Otsu, Japan. |
| authorships[1].author.id | https://openalex.org/A5018175714 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-0705-9449 |
| authorships[1].author.display_name | Yoshihiro Kokubo |
| authorships[1].countries | JP |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210111404 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan. |
| authorships[1].institutions[0].id | https://openalex.org/I4210111404 |
| authorships[1].institutions[0].ror | https://ror.org/01v55qb38 |
| authorships[1].institutions[0].type | healthcare |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210111404 |
| authorships[1].institutions[0].country_code | JP |
| authorships[1].institutions[0].display_name | National Cerebral and Cardiovascular Center |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Yoshihiro Kokubo |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan. |
| authorships[2].author.id | https://openalex.org/A5016090445 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-3204-1627 |
| authorships[2].author.display_name | Mai Inoue |
| authorships[2].countries | JP |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I4210086383 |
| authorships[2].affiliations[0].raw_affiliation_string | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457. |
| authorships[2].institutions[0].id | https://openalex.org/I4210086383 |
| authorships[2].institutions[0].ror | https://ror.org/001rkbe13 |
| authorships[2].institutions[0].type | facility |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210086383 |
| authorships[2].institutions[0].country_code | JP |
| authorships[2].institutions[0].display_name | National Institute of Biomedical Innovation, Health and Nutrition |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Mai Inoue |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457. |
| authorships[3].author.id | https://openalex.org/A5101818222 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-9179-6080 |
| authorships[3].author.display_name | Masaki Yamamoto |
| authorships[3].countries | JP |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210086383 |
| authorships[3].affiliations[0].raw_affiliation_string | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457. |
| authorships[3].institutions[0].id | https://openalex.org/I4210086383 |
| authorships[3].institutions[0].ror | https://ror.org/001rkbe13 |
| authorships[3].institutions[0].type | facility |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210086383 |
| authorships[3].institutions[0].country_code | JP |
| authorships[3].institutions[0].display_name | National Institute of Biomedical Innovation, Health and Nutrition |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Masaki Yamamoto |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457. |
| authorships[4].author.id | https://openalex.org/A5049385580 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-0690-8012 |
| authorships[4].author.display_name | Attayeb Mohsen |
| authorships[4].countries | JP, LY |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4210161365 |
| authorships[4].affiliations[0].raw_affiliation_string | Libyan Centre for Dental Research, Zliten, Libya. |
| authorships[4].affiliations[1].institution_ids | https://openalex.org/I4210086383 |
| authorships[4].affiliations[1].raw_affiliation_string | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457. |
| authorships[4].institutions[0].id | https://openalex.org/I4210086383 |
| authorships[4].institutions[0].ror | https://ror.org/001rkbe13 |
| authorships[4].institutions[0].type | facility |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210086383 |
| authorships[4].institutions[0].country_code | JP |
| authorships[4].institutions[0].display_name | National Institute of Biomedical Innovation, Health and Nutrition |
| authorships[4].institutions[1].id | https://openalex.org/I4210161365 |
| authorships[4].institutions[1].ror | https://ror.org/05s3qq987 |
| authorships[4].institutions[1].type | education |
| authorships[4].institutions[1].lineage | https://openalex.org/I4210161365 |
| authorships[4].institutions[1].country_code | LY |
| authorships[4].institutions[1].display_name | Libyan Academy |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Attayeb Mohsen |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457., Libyan Centre for Dental Research, Zliten, Libya. |
| authorships[5].author.id | https://openalex.org/A5055926370 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-3564-4776 |
| authorships[5].author.display_name | Agustin Martin‐Morales |
| authorships[5].countries | JP |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I4210086383 |
| authorships[5].affiliations[0].raw_affiliation_string | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457. |
| authorships[5].institutions[0].id | https://openalex.org/I4210086383 |
| authorships[5].institutions[0].ror | https://ror.org/001rkbe13 |
| authorships[5].institutions[0].type | facility |
| authorships[5].institutions[0].lineage | https://openalex.org/I4210086383 |
| authorships[5].institutions[0].country_code | JP |
| authorships[5].institutions[0].display_name | National Institute of Biomedical Innovation, Health and Nutrition |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Agustin Martin-Morales |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457. |
| authorships[6].author.id | https://openalex.org/A5040499657 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-3524-1459 |
| authorships[6].author.display_name | Research Dawadi |
| authorships[6].countries | JP |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I4210086383 |
| authorships[6].affiliations[0].raw_affiliation_string | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457. |
| authorships[6].institutions[0].id | https://openalex.org/I4210086383 |
| authorships[6].institutions[0].ror | https://ror.org/001rkbe13 |
| authorships[6].institutions[0].type | facility |
| authorships[6].institutions[0].lineage | https://openalex.org/I4210086383 |
| authorships[6].institutions[0].country_code | JP |
| authorships[6].institutions[0].display_name | National Institute of Biomedical Innovation, Health and Nutrition |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Research Dawadi |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457. |
| authorships[7].author.id | https://openalex.org/A5000257861 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-2080-7480 |
| authorships[7].author.display_name | Takao Inoué |
| authorships[7].countries | JP |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I4210097831 |
| authorships[7].affiliations[0].raw_affiliation_string | Faculty of Informatics, Yamato University, Osaka, Japan. |
| authorships[7].affiliations[1].institution_ids | https://openalex.org/I4210086383 |
| authorships[7].affiliations[1].raw_affiliation_string | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457. |
| authorships[7].institutions[0].id | https://openalex.org/I4210086383 |
| authorships[7].institutions[0].ror | https://ror.org/001rkbe13 |
| authorships[7].institutions[0].type | facility |
| authorships[7].institutions[0].lineage | https://openalex.org/I4210086383 |
| authorships[7].institutions[0].country_code | JP |
| authorships[7].institutions[0].display_name | National Institute of Biomedical Innovation, Health and Nutrition |
| authorships[7].institutions[1].id | https://openalex.org/I4210097831 |
| authorships[7].institutions[1].ror | https://ror.org/00wwj8r66 |
| authorships[7].institutions[1].type | education |
| authorships[7].institutions[1].lineage | https://openalex.org/I4210097831 |
| authorships[7].institutions[1].country_code | JP |
| authorships[7].institutions[1].display_name | Yamato University |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Takao Inoue |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457., Faculty of Informatics, Yamato University, Osaka, Japan. |
| authorships[8].author.id | https://openalex.org/A5112400552 |
| authorships[8].author.orcid | |
| authorships[8].author.display_name | J. Tay |
| authorships[8].countries | JP |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I4210086383 |
| authorships[8].affiliations[0].raw_affiliation_string | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457. |
| authorships[8].institutions[0].id | https://openalex.org/I4210086383 |
| authorships[8].institutions[0].ror | https://ror.org/001rkbe13 |
| authorships[8].institutions[0].type | facility |
| authorships[8].institutions[0].lineage | https://openalex.org/I4210086383 |
| authorships[8].institutions[0].country_code | JP |
| authorships[8].institutions[0].display_name | National Institute of Biomedical Innovation, Health and Nutrition |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Jie Ting Tay |
| authorships[8].is_corresponding | False |
| authorships[8].raw_affiliation_strings | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457. |
| authorships[9].author.id | https://openalex.org/A5094204411 |
| authorships[9].author.orcid | https://orcid.org/0009-0002-5031-0632 |
| authorships[9].author.display_name | Mari Yoshizaki |
| authorships[9].countries | JP |
| authorships[9].affiliations[0].institution_ids | https://openalex.org/I4210086383 |
| authorships[9].affiliations[0].raw_affiliation_string | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457. |
| authorships[9].institutions[0].id | https://openalex.org/I4210086383 |
| authorships[9].institutions[0].ror | https://ror.org/001rkbe13 |
| authorships[9].institutions[0].type | facility |
| authorships[9].institutions[0].lineage | https://openalex.org/I4210086383 |
| authorships[9].institutions[0].country_code | JP |
| authorships[9].institutions[0].display_name | National Institute of Biomedical Innovation, Health and Nutrition |
| authorships[9].author_position | middle |
| authorships[9].raw_author_name | Mari Yoshizaki |
| authorships[9].is_corresponding | False |
| authorships[9].raw_affiliation_strings | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457. |
| authorships[10].author.id | https://openalex.org/A5101617144 |
| authorships[10].author.orcid | https://orcid.org/0000-0002-6418-991X |
| authorships[10].author.display_name | Naoki Watanabe |
| authorships[10].countries | JP |
| authorships[10].affiliations[0].institution_ids | https://openalex.org/I4210086383 |
| authorships[10].affiliations[0].raw_affiliation_string | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457. |
| authorships[10].institutions[0].id | https://openalex.org/I4210086383 |
| authorships[10].institutions[0].ror | https://ror.org/001rkbe13 |
| authorships[10].institutions[0].type | facility |
| authorships[10].institutions[0].lineage | https://openalex.org/I4210086383 |
| authorships[10].institutions[0].country_code | JP |
| authorships[10].institutions[0].display_name | National Institute of Biomedical Innovation, Health and Nutrition |
| authorships[10].author_position | middle |
| authorships[10].raw_author_name | Naoki Watanabe |
| authorships[10].is_corresponding | False |
| authorships[10].raw_affiliation_strings | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457. |
| authorships[11].author.id | https://openalex.org/A5080028788 |
| authorships[11].author.orcid | https://orcid.org/0000-0001-5118-7803 |
| authorships[11].author.display_name | Yuki Kuriya |
| authorships[11].countries | JP |
| authorships[11].affiliations[0].institution_ids | https://openalex.org/I4210086383 |
| authorships[11].affiliations[0].raw_affiliation_string | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457. |
| authorships[11].institutions[0].id | https://openalex.org/I4210086383 |
| authorships[11].institutions[0].ror | https://ror.org/001rkbe13 |
| authorships[11].institutions[0].type | facility |
| authorships[11].institutions[0].lineage | https://openalex.org/I4210086383 |
| authorships[11].institutions[0].country_code | JP |
| authorships[11].institutions[0].display_name | National Institute of Biomedical Innovation, Health and Nutrition |
| authorships[11].author_position | middle |
| authorships[11].raw_author_name | Yuki Kuriya |
| authorships[11].is_corresponding | False |
| authorships[11].raw_affiliation_strings | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457. |
| authorships[12].author.id | https://openalex.org/A5103232252 |
| authorships[12].author.orcid | https://orcid.org/0000-0002-8066-8363 |
| authorships[12].author.display_name | Chisa Matsumoto |
| authorships[12].countries | JP |
| authorships[12].affiliations[0].institution_ids | https://openalex.org/I4210108955 |
| authorships[12].affiliations[0].raw_affiliation_string | Department of Cardiology, Center for Health Surveillance and Preventive Medicine, Tokyo Medical University Hospital, Tokyo, Japan. |
| authorships[12].affiliations[1].institution_ids | https://openalex.org/I4210111404 |
| authorships[12].affiliations[1].raw_affiliation_string | Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan. |
| authorships[12].institutions[0].id | https://openalex.org/I4210111404 |
| authorships[12].institutions[0].ror | https://ror.org/01v55qb38 |
| authorships[12].institutions[0].type | healthcare |
| authorships[12].institutions[0].lineage | https://openalex.org/I4210111404 |
| authorships[12].institutions[0].country_code | JP |
| authorships[12].institutions[0].display_name | National Cerebral and Cardiovascular Center |
| authorships[12].institutions[1].id | https://openalex.org/I4210108955 |
| authorships[12].institutions[1].ror | https://ror.org/012e6rh19 |
| authorships[12].institutions[1].type | healthcare |
| authorships[12].institutions[1].lineage | https://openalex.org/I4210108955 |
| authorships[12].institutions[1].country_code | JP |
| authorships[12].institutions[1].display_name | Tokyo Medical University Hospital |
| authorships[12].author_position | middle |
| authorships[12].raw_author_name | Chisa Matsumoto |
| authorships[12].is_corresponding | False |
| authorships[12].raw_affiliation_strings | Department of Cardiology, Center for Health Surveillance and Preventive Medicine, Tokyo Medical University Hospital, Tokyo, Japan., Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan. |
| authorships[13].author.id | https://openalex.org/A5027897280 |
| authorships[13].author.orcid | https://orcid.org/0000-0002-3335-2243 |
| authorships[13].author.display_name | Ahmed Arafa |
| authorships[13].countries | EG, JP |
| authorships[13].affiliations[0].institution_ids | https://openalex.org/I4210111404 |
| authorships[13].affiliations[0].raw_affiliation_string | Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan. |
| authorships[13].affiliations[1].institution_ids | https://openalex.org/I113643904 |
| authorships[13].affiliations[1].raw_affiliation_string | Department of Public Health, Faculty of Medicine, Beni-Suef University, Beni-Suef, Egypt. |
| authorships[13].institutions[0].id | https://openalex.org/I113643904 |
| authorships[13].institutions[0].ror | https://ror.org/05pn4yv70 |
| authorships[13].institutions[0].type | education |
| authorships[13].institutions[0].lineage | https://openalex.org/I113643904 |
| authorships[13].institutions[0].country_code | EG |
| authorships[13].institutions[0].display_name | Beni-Suef University |
| authorships[13].institutions[1].id | https://openalex.org/I4210111404 |
| authorships[13].institutions[1].ror | https://ror.org/01v55qb38 |
| authorships[13].institutions[1].type | healthcare |
| authorships[13].institutions[1].lineage | https://openalex.org/I4210111404 |
| authorships[13].institutions[1].country_code | JP |
| authorships[13].institutions[1].display_name | National Cerebral and Cardiovascular Center |
| authorships[13].author_position | middle |
| authorships[13].raw_author_name | Ahmed Arafa |
| authorships[13].is_corresponding | False |
| authorships[13].raw_affiliation_strings | Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan., Department of Public Health, Faculty of Medicine, Beni-Suef University, Beni-Suef, Egypt. |
| authorships[14].author.id | https://openalex.org/A5088462615 |
| authorships[14].author.orcid | https://orcid.org/0000-0002-3627-5626 |
| authorships[14].author.display_name | Yoko M. Nakao |
| authorships[14].countries | JP |
| authorships[14].affiliations[0].institution_ids | https://openalex.org/I4210111404 |
| authorships[14].affiliations[0].raw_affiliation_string | Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan. |
| authorships[14].institutions[0].id | https://openalex.org/I4210111404 |
| authorships[14].institutions[0].ror | https://ror.org/01v55qb38 |
| authorships[14].institutions[0].type | healthcare |
| authorships[14].institutions[0].lineage | https://openalex.org/I4210111404 |
| authorships[14].institutions[0].country_code | JP |
| authorships[14].institutions[0].display_name | National Cerebral and Cardiovascular Center |
| authorships[14].author_position | middle |
| authorships[14].raw_author_name | Yoko M Nakao |
| authorships[14].is_corresponding | False |
| authorships[14].raw_affiliation_strings | Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan. |
| authorships[15].author.id | https://openalex.org/A5117525072 |
| authorships[15].author.orcid | https://orcid.org/0009-0009-2739-9609 |
| authorships[15].author.display_name | Yuka Kato |
| authorships[15].countries | JP |
| authorships[15].affiliations[0].institution_ids | https://openalex.org/I98285908 |
| authorships[15].affiliations[0].raw_affiliation_string | Division of Health Sciences, Osaka University Graduate School of Medicine, Suita, Osaka, Japan. |
| authorships[15].affiliations[1].institution_ids | https://openalex.org/I4210111404 |
| authorships[15].affiliations[1].raw_affiliation_string | Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan. |
| authorships[15].institutions[0].id | https://openalex.org/I4210111404 |
| authorships[15].institutions[0].ror | https://ror.org/01v55qb38 |
| authorships[15].institutions[0].type | healthcare |
| authorships[15].institutions[0].lineage | https://openalex.org/I4210111404 |
| authorships[15].institutions[0].country_code | JP |
| authorships[15].institutions[0].display_name | National Cerebral and Cardiovascular Center |
| authorships[15].institutions[1].id | https://openalex.org/I98285908 |
| authorships[15].institutions[1].ror | https://ror.org/035t8zc32 |
| authorships[15].institutions[1].type | education |
| authorships[15].institutions[1].lineage | https://openalex.org/I98285908 |
| authorships[15].institutions[1].country_code | JP |
| authorships[15].institutions[1].display_name | The University of Osaka |
| authorships[15].author_position | middle |
| authorships[15].raw_author_name | Yuka Kato |
| authorships[15].is_corresponding | False |
| authorships[15].raw_affiliation_strings | Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan., Division of Health Sciences, Osaka University Graduate School of Medicine, Suita, Osaka, Japan. |
| authorships[16].author.id | https://openalex.org/A5007699908 |
| authorships[16].author.orcid | https://orcid.org/0000-0002-2318-2447 |
| authorships[16].author.display_name | Masayuki Teramoto |
| authorships[16].countries | JP |
| authorships[16].affiliations[0].institution_ids | https://openalex.org/I4210111404 |
| authorships[16].affiliations[0].raw_affiliation_string | Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan. |
| authorships[16].institutions[0].id | https://openalex.org/I4210111404 |
| authorships[16].institutions[0].ror | https://ror.org/01v55qb38 |
| authorships[16].institutions[0].type | healthcare |
| authorships[16].institutions[0].lineage | https://openalex.org/I4210111404 |
| authorships[16].institutions[0].country_code | JP |
| authorships[16].institutions[0].display_name | National Cerebral and Cardiovascular Center |
| authorships[16].author_position | middle |
| authorships[16].raw_author_name | Masayuki Teramoto |
| authorships[16].is_corresponding | False |
| authorships[16].raw_affiliation_strings | Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan. |
| authorships[17].author.id | https://openalex.org/A5037239790 |
| authorships[17].author.orcid | https://orcid.org/0000-0002-6686-4018 |
| authorships[17].author.display_name | Michihiro Araki |
| authorships[17].countries | JP |
| authorships[17].affiliations[0].institution_ids | https://openalex.org/I4210086383 |
| authorships[17].affiliations[0].raw_affiliation_string | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457. |
| authorships[17].affiliations[1].institution_ids | https://openalex.org/I65837984 |
| authorships[17].affiliations[1].raw_affiliation_string | Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Japan. |
| authorships[17].affiliations[2].institution_ids | https://openalex.org/I22299242 |
| authorships[17].affiliations[2].raw_affiliation_string | Graduate School of Medicine, Kyoto University, Kyoto, Japan. |
| authorships[17].institutions[0].id | https://openalex.org/I65837984 |
| authorships[17].institutions[0].ror | https://ror.org/03tgsfw79 |
| authorships[17].institutions[0].type | education |
| authorships[17].institutions[0].lineage | https://openalex.org/I65837984 |
| authorships[17].institutions[0].country_code | JP |
| authorships[17].institutions[0].display_name | Kobe University |
| authorships[17].institutions[1].id | https://openalex.org/I22299242 |
| authorships[17].institutions[1].ror | https://ror.org/02kpeqv85 |
| authorships[17].institutions[1].type | education |
| authorships[17].institutions[1].lineage | https://openalex.org/I22299242 |
| authorships[17].institutions[1].country_code | JP |
| authorships[17].institutions[1].display_name | Kyoto University |
| authorships[17].institutions[2].id | https://openalex.org/I4210086383 |
| authorships[17].institutions[2].ror | https://ror.org/001rkbe13 |
| authorships[17].institutions[2].type | facility |
| authorships[17].institutions[2].lineage | https://openalex.org/I4210086383 |
| authorships[17].institutions[2].country_code | JP |
| authorships[17].institutions[2].display_name | National Institute of Biomedical Innovation, Health and Nutrition |
| authorships[17].author_position | last |
| authorships[17].raw_author_name | Michihiro Araki |
| authorships[17].is_corresponding | False |
| authorships[17].raw_affiliation_strings | Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457., Graduate School of Medicine, Kyoto University, Kyoto, Japan., Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Japan. |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.2196/68066 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-02-24T00:00:00 |
| display_name | Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population–Based Study |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11396 |
| primary_topic.field.id | https://openalex.org/fields/36 |
| primary_topic.field.display_name | Health Professions |
| primary_topic.score | 0.9973999857902527 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/3605 |
| primary_topic.subfield.display_name | Health Information Management |
| primary_topic.display_name | Artificial Intelligence in Healthcare |
| related_works | https://openalex.org/W4391375266, https://openalex.org/W2899084033, https://openalex.org/W2748952813, https://openalex.org/W3031052312, https://openalex.org/W4389568370, https://openalex.org/W3032375762, https://openalex.org/W1995515455, https://openalex.org/W2080531066, https://openalex.org/W3108674512, https://openalex.org/W1506200166 |
| cited_by_count | 10 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 10 |
| locations_count | 4 |
| best_oa_location.id | doi:10.2196/68066 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210231566 |
| best_oa_location.source.issn | 2561-1011 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2561-1011 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | JMIR Cardio |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | JMIR Cardio |
| best_oa_location.landing_page_url | https://doi.org/10.2196/68066 |
| primary_location.id | doi:10.2196/68066 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210231566 |
| primary_location.source.issn | 2561-1011 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2561-1011 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | JMIR Cardio |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | JMIR Cardio |
| primary_location.landing_page_url | https://doi.org/10.2196/68066 |
| publication_date | 2025-02-24 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W3092849554, https://openalex.org/W3113178943, https://openalex.org/W4285596437, https://openalex.org/W4308698382, https://openalex.org/W2605253636, https://openalex.org/W3025161810, https://openalex.org/W3116286104, https://openalex.org/W4400195120, https://openalex.org/W4386601989, https://openalex.org/W4405104656, https://openalex.org/W4205941964, https://openalex.org/W4312085640, https://openalex.org/W4281809599, https://openalex.org/W2953505302, https://openalex.org/W4394857110, https://openalex.org/W2031498851, https://openalex.org/W2145850334, https://openalex.org/W2080093358, https://openalex.org/W2043066499, https://openalex.org/W2806791077, https://openalex.org/W3137854130, https://openalex.org/W3012413426, https://openalex.org/W1789016606, https://openalex.org/W3046184118, https://openalex.org/W2508494164, https://openalex.org/W1967542410, https://openalex.org/W4407633893, https://openalex.org/W2977262858, https://openalex.org/W2797636288, https://openalex.org/W3133501805, https://openalex.org/W2023487854, https://openalex.org/W1966886574, https://openalex.org/W3198302963, https://openalex.org/W3034071691, https://openalex.org/W3130761441, https://openalex.org/W3115251202, https://openalex.org/W2973819558, https://openalex.org/W3216171685, https://openalex.org/W3129073933, https://openalex.org/W4297522372, https://openalex.org/W3129433473, https://openalex.org/W2168205528, https://openalex.org/W2467087145, https://openalex.org/W2904356389, https://openalex.org/W2029421987, https://openalex.org/W2054995207, https://openalex.org/W2332258883, https://openalex.org/W2783712034, https://openalex.org/W3211697849, https://openalex.org/W4281692358, https://openalex.org/W4323644141, https://openalex.org/W4230932206, https://openalex.org/W2989281995 |
| referenced_works_count | 53 |
| abstract_inverted_index.1 | 175 |
| abstract_inverted_index.A | 27, 115 |
| abstract_inverted_index.F | 174 |
| abstract_inverted_index.a | 7, 274, 343, 367 |
| abstract_inverted_index.15 | 107 |
| abstract_inverted_index.28 | 121 |
| abstract_inverted_index.CI | 230, 236, 242, 253 |
| abstract_inverted_index.ML | 84, 140, 373 |
| abstract_inverted_index.RF | 218, 255 |
| abstract_inverted_index.We | 365 |
| abstract_inverted_index.an | 104, 225, 245 |
| abstract_inverted_index.as | 279, 323 |
| abstract_inverted_index.by | 281 |
| abstract_inverted_index.go | 50 |
| abstract_inverted_index.in | 125, 348, 359, 396 |
| abstract_inverted_index.is | 6, 19 |
| abstract_inverted_index.of | 10, 47, 54, 70, 106, 117, 201, 227, 233, 239, 250, 276, 290, 297, 386 |
| abstract_inverted_index.on | 59, 77 |
| abstract_inverted_index.to | 37, 66, 74, 192, 205, 326, 369, 391 |
| abstract_inverted_index.305 | 211 |
| abstract_inverted_index.CHD | 48, 159, 327, 371, 387 |
| abstract_inverted_index.The | 87, 217 |
| abstract_inverted_index.and | 12, 25, 41, 80, 101, 120, 135, 152, 181, 197, 244, 268, 312, 314, 338, 374, 400 |
| abstract_inverted_index.fat | 340 |
| abstract_inverted_index.for | 21, 112, 157, 354, 378 |
| abstract_inverted_index.key | 16, 288 |
| abstract_inverted_index.net | 271 |
| abstract_inverted_index.the | 52, 68, 126, 172, 194, 199, 221, 248, 293, 298, 351, 383 |
| abstract_inverted_index.was | 163, 346 |
| abstract_inverted_index.(95% | 229, 235, 241, 252 |
| abstract_inverted_index.(ML) | 33 |
| abstract_inverted_index.0.72 | 240 |
| abstract_inverted_index.0.73 | 228, 251 |
| abstract_inverted_index.0.74 | 234 |
| abstract_inverted_index.1989 | 100 |
| abstract_inverted_index.7260 | 118, 209 |
| abstract_inverted_index.7672 | 90 |
| abstract_inverted_index.CHD, | 75, 291 |
| abstract_inverted_index.CHD. | 206, 216 |
| abstract_inverted_index.Five | 139 |
| abstract_inverted_index.Over | 103 |
| abstract_inverted_index.SHAP | 285, 376 |
| abstract_inverted_index.This | 63, 380 |
| abstract_inverted_index.aged | 92 |
| abstract_inverted_index.aims | 65 |
| abstract_inverted_index.also | 256 |
| abstract_inverted_index.area | 170, 246 |
| abstract_inverted_index.body | 339 |
| abstract_inverted_index.both | 78 |
| abstract_inverted_index.care | 394 |
| abstract_inverted_index.cell | 336 |
| abstract_inverted_index.data | 134 |
| abstract_inverted_index.from | 95 |
| abstract_inverted_index.rely | 58 |
| abstract_inverted_index.risk | 17, 23, 72, 203, 277, 320, 328, 356, 388, 398 |
| abstract_inverted_index.that | 49 |
| abstract_inverted_index.used | 191 |
| abstract_inverted_index.were | 110, 123, 190, 213 |
| abstract_inverted_index.with | 131, 215, 224, 260, 265 |
| abstract_inverted_index.(CHD) | 5 |
| abstract_inverted_index.(RF), | 145 |
| abstract_inverted_index.1999. | 102 |
| abstract_inverted_index.30-84 | 93 |
| abstract_inverted_index.Among | 208 |
| abstract_inverted_index.City, | 97 |
| abstract_inverted_index.Light | 153 |
| abstract_inverted_index.Model | 161 |
| abstract_inverted_index.Novel | 319 |
| abstract_inverted_index.Suita | 96 |
| abstract_inverted_index.after | 128 |
| abstract_inverted_index.blood | 302, 335 |
| abstract_inverted_index.cause | 9 |
| abstract_inverted_index.curve | 183, 249, 283 |
| abstract_inverted_index.heart | 3 |
| abstract_inverted_index.lipid | 304 |
| abstract_inverted_index.lower | 330 |
| abstract_inverted_index.major | 8 |
| abstract_inverted_index.model | 219, 368 |
| abstract_inverted_index.novel | 45, 81 |
| abstract_inverted_index.range | 275 |
| abstract_inverted_index.rate. | 318 |
| abstract_inverted_index.study | 64, 88 |
| abstract_inverted_index.total | 116 |
| abstract_inverted_index.under | 171, 247 |
| abstract_inverted_index.using | 30, 83, 165, 372 |
| abstract_inverted_index.which | 57 |
| abstract_inverted_index.white | 334 |
| abstract_inverted_index.years | 94 |
| abstract_inverted_index.(4.2%) | 212 |
| abstract_inverted_index.Japan, | 98 |
| abstract_inverted_index.across | 273 |
| abstract_inverted_index.aiming | 390 |
| abstract_inverted_index.beyond | 51 |
| abstract_inverted_index.common | 299 |
| abstract_inverted_index.curve, | 173 |
| abstract_inverted_index.effect | 345 |
| abstract_inverted_index.forest | 144 |
| abstract_inverted_index.future | 360 |
| abstract_inverted_index.health | 362, 393 |
| abstract_inverted_index.models | 196 |
| abstract_inverted_index.nature | 385 |
| abstract_inverted_index.offers | 34 |
| abstract_inverted_index.random | 143 |
| abstract_inverted_index.showed | 257 |
| abstract_inverted_index.vector | 147 |
| abstract_inverted_index.women, | 349 |
| abstract_inverted_index.years, | 108 |
| abstract_inverted_index.(SHAPs) | 189 |
| abstract_inverted_index.-score, | 176 |
| abstract_inverted_index.Extreme | 149 |
| abstract_inverted_index.Methods | 86 |
| abstract_inverted_index.Results | 207 |
| abstract_inverted_index.Shapley | 186 |
| abstract_inverted_index.analyze | 38 |
| abstract_inverted_index.applied | 156, 375 |
| abstract_inverted_index.artery, | 301 |
| abstract_inverted_index.average | 105 |
| abstract_inverted_index.benefit | 272 |
| abstract_inverted_index.between | 99 |
| abstract_inverted_index.calcium | 331 |
| abstract_inverted_index.carotid | 300 |
| abstract_inverted_index.closely | 263 |
| abstract_inverted_index.counts, | 337 |
| abstract_inverted_index.curves, | 178 |
| abstract_inverted_index.disease | 4 |
| abstract_inverted_index.events. | 114 |
| abstract_inverted_index.factors | 18, 73, 204, 321, 399 |
| abstract_inverted_index.highest | 222 |
| abstract_inverted_index.levels, | 332 |
| abstract_inverted_index.machine | 31 |
| abstract_inverted_index.markers | 82 |
| abstract_inverted_index.methods | 377 |
| abstract_inverted_index.missing | 132 |
| abstract_inverted_index.models, | 56 |
| abstract_inverted_index.outcome | 133 |
| abstract_inverted_index.predict | 370 |
| abstract_inverted_index.ratios, | 180 |
| abstract_inverted_index.support | 146, 392 |
| abstract_inverted_index.various | 71, 202 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Additive | 187 |
| abstract_inverted_index.Coronary | 2 |
| abstract_inverted_index.Gradient | 150 |
| abstract_inverted_index.accuracy | 226 |
| abstract_inverted_index.advanced | 35 |
| abstract_inverted_index.aligning | 264 |
| abstract_inverted_index.analysis | 127, 286 |
| abstract_inverted_index.approach | 29, 381 |
| abstract_inverted_index.complex, | 39 |
| abstract_inverted_index.decision | 182, 282 |
| abstract_inverted_index.elevated | 333 |
| abstract_inverted_index.evaluate | 67 |
| abstract_inverted_index.focusing | 76 |
| abstract_inverted_index.included | 124, 329 |
| abstract_inverted_index.learning | 32 |
| abstract_inverted_index.machine, | 148 |
| abstract_inverted_index.observed | 266, 347 |
| abstract_inverted_index.profiles | 305 |
| abstract_inverted_index.provided | 269 |
| abstract_inverted_index.Boosting, | 151 |
| abstract_inverted_index.Objective | 62 |
| abstract_inverted_index.accuracy, | 166 |
| abstract_inverted_index.analysis. | 184, 284 |
| abstract_inverted_index.datasets, | 43 |
| abstract_inverted_index.developed | 366 |
| abstract_inverted_index.diagnosed | 214 |
| abstract_inverted_index.effective | 22, 402 |
| abstract_inverted_index.essential | 20 |
| abstract_inverted_index.estimated | 315 |
| abstract_inverted_index.evaluated | 164 |
| abstract_inverted_index.excellent | 258 |
| abstract_inverted_index.excluding | 129 |
| abstract_inverted_index.including | 292 |
| abstract_inverted_index.interpret | 193 |
| abstract_inverted_index.monitored | 111 |
| abstract_inverted_index.morbidity | 11 |
| abstract_inverted_index.mortality | 13 |
| abstract_inverted_index.necessity | 353 |
| abstract_inverted_index.outcomes, | 267 |
| abstract_inverted_index.potential | 352 |
| abstract_inverted_index.predicted | 261 |
| abstract_inverted_index.pressure, | 303 |
| abstract_inverted_index.recruited | 89 |
| abstract_inverted_index.thickness | 295 |
| abstract_inverted_index.variables | 122 |
| abstract_inverted_index.(IMT_cMax) | 296 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.assessment | 24, 357 |
| abstract_inverted_index.elucidated | 287 |
| abstract_inverted_index.filtration | 317 |
| abstract_inverted_index.glomerular | 316 |
| abstract_inverted_index.highlights | 382 |
| abstract_inverted_index.identified | 322 |
| abstract_inverted_index.incidence. | 160 |
| abstract_inverted_index.nonlinear, | 40 |
| abstract_inverted_index.precision, | 169 |
| abstract_inverted_index.predefined | 60 |
| abstract_inverted_index.predicting | 158 |
| abstract_inverted_index.prediction | 195 |
| abstract_inverted_index.predictors | 46, 289 |
| abstract_inverted_index.prevention | 403 |
| abstract_inverted_index.protective | 344 |
| abstract_inverted_index.strategies | 358 |
| abstract_inverted_index.suggesting | 350 |
| abstract_inverted_index.techniques | 36 |
| abstract_inverted_index.uncovering | 44 |
| abstract_inverted_index.understand | 198 |
| abstract_inverted_index.variables. | 61, 138 |
| abstract_inverted_index.worldwide. | 14 |
| abstract_inverted_index.Conclusions | 364 |
| abstract_inverted_index.Identifying | 15 |
| abstract_inverted_index.calibration | 177 |
| abstract_inverted_index.data-driven | 28 |
| abstract_inverted_index.eliminating | 136 |
| abstract_inverted_index.established | 79 |
| abstract_inverted_index.evaluation, | 389 |
| abstract_inverted_index.formulating | 401 |
| abstract_inverted_index.identifying | 397 |
| abstract_inverted_index.individuals | 130 |
| abstract_inverted_index.limitations | 53 |
| abstract_inverted_index.lipoprotein | 307, 310 |
| abstract_inverted_index.multifactor | 384 |
| abstract_inverted_index.percentage. | 341 |
| abstract_inverted_index.performance | 162 |
| abstract_inverted_index.prevention. | 26 |
| abstract_inverted_index.regression, | 142 |
| abstract_inverted_index.sensitivity | 232 |
| abstract_inverted_index.significant | 324 |
| abstract_inverted_index.specificity | 238 |
| abstract_inverted_index.strategies. | 404 |
| abstract_inverted_index.substantial | 270 |
| abstract_inverted_index.techniques. | 85 |
| abstract_inverted_index.thresholds, | 278 |
| abstract_inverted_index.traditional | 55 |
| abstract_inverted_index.unnecessary | 137 |
| abstract_inverted_index.Explanations | 188 |
| abstract_inverted_index.Furthermore, | 342 |
| abstract_inverted_index.calibration, | 259 |
| abstract_inverted_index.cholesterol, | 308, 311 |
| abstract_inverted_index.contribution | 69, 200 |
| abstract_inverted_index.contributors | 325 |
| abstract_inverted_index.demonstrated | 220, 280 |
| abstract_inverted_index.evaluations. | 363 |
| abstract_inverted_index.high-density | 309 |
| abstract_inverted_index.intima-media | 294 |
| abstract_inverted_index.participants | 91, 109, 119 |
| abstract_inverted_index.performance, | 223 |
| abstract_inverted_index.sensitivity, | 167 |
| abstract_inverted_index.specificity, | 168 |
| abstract_inverted_index.0.61‐0.83), | 243 |
| abstract_inverted_index.0.62‐0.84), | 237 |
| abstract_inverted_index.0.64‐0.80), | 231 |
| abstract_inverted_index.0.65‐0.80). | 254 |
| abstract_inverted_index.Additionally, | 185 |
| abstract_inverted_index.participants, | 210 |
| abstract_inverted_index.probabilities | 262 |
| abstract_inverted_index.professionals | 395 |
| abstract_inverted_index.Machine—were | 155 |
| abstract_inverted_index.cardiovascular | 113, 361 |
| abstract_inverted_index.gender-specific | 355 |
| abstract_inverted_index.interpretation. | 379 |
| abstract_inverted_index.triglycerides), | 313 |
| abstract_inverted_index.high-dimensional | 42 |
| abstract_inverted_index.Gradient-Boosting | 154 |
| abstract_inverted_index.models—logistic | 141 |
| abstract_inverted_index.(non–high-density | 306 |
| abstract_inverted_index.observed-to-expected | 179 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 4 |
| institutions_distinct_count | 18 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.7099999785423279 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.99633748 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |