Comparison between Machine Learning and Multiple Linear Regression to Identify Abnormal Thallium Myocardial Perfusion Scan in Chinese Type 2 Diabetes Article Swipe
Jiunn‐Diann Lin
,
Dee Pei
,
Fang-Yu Chen
,
Chung‐Ze Wu
,
Chieh‐Hua Lu
,
Li-Ying Huang
,
Chun‐Heng Kuo
,
Shi‐Wen Kuo
,
Yen‐Lin Chen
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics12071619
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics12071619
Type 2 diabetes mellitus (T2DM) patients have a high risk of coronary artery disease (CAD). Thallium-201 myocardial perfusion scan (Th-201 scan) is a non-invasive and extensively used tool in recognizing CAD in clinical settings. In this study, we attempted to compare the predictive accuracy of evaluating abnormal Th-201 scans using traditional multiple linear regression (MLR) with four machine learning (ML) methods. From the study, we can determine whether ML surpasses traditional MLR and rank the clinical variables and compare them with previous reports.In total, 796 T2DM, including 368 men and 528 women, were enrolled. In addition to traditional MLR, classification and regression tree (CART), random forest (RF), stochastic gradient boosting (SGB) and eXtreme gradient boosting (XGBoost) were also used to analyze abnormal Th-201 scans. Stress sum score was used as the endpoint (dependent variable). Our findings show that all four root mean square errors of ML are smaller than with MLR, which implies that ML is more precise than MLR in determining abnormal Th-201 scans by using clinical parameters. The first seven factors, from the most important to the least are:body mass index, hemoglobin, age, glycated hemoglobin, Creatinine, systolic and diastolic blood pressure. In conclusion, ML is not inferior to traditional MLR in predicting abnormal Th-201 scans, and the most important factors are body mass index, hemoglobin, age, glycated hemoglobin, creatinine, systolic and diastolic blood pressure. ML methods are superior in these kinds of studies.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/diagnostics12071619
- https://www.mdpi.com/2075-4418/12/7/1619/pdf?version=1657176881
- OA Status
- gold
- Cited By
- 6
- References
- 75
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4283803068
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4283803068Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/diagnostics12071619Digital Object Identifier
- Title
-
Comparison between Machine Learning and Multiple Linear Regression to Identify Abnormal Thallium Myocardial Perfusion Scan in Chinese Type 2 DiabetesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-03Full publication date if available
- Authors
-
Jiunn‐Diann Lin, Dee Pei, Fang-Yu Chen, Chung‐Ze Wu, Chieh‐Hua Lu, Li-Ying Huang, Chun‐Heng Kuo, Shi‐Wen Kuo, Yen‐Lin ChenList of authors in order
- Landing page
-
https://doi.org/10.3390/diagnostics12071619Publisher landing page
- PDF URL
-
https://www.mdpi.com/2075-4418/12/7/1619/pdf?version=1657176881Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2075-4418/12/7/1619/pdf?version=1657176881Direct OA link when available
- Concepts
-
Glycated hemoglobin, Medicine, Internal medicine, Cardiology, Creatinine, Coronary artery disease, Linear regression, Body mass index, Blood pressure, Diabetes mellitus, Type 2 diabetes, Mathematics, Statistics, EndocrinologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 3, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
75Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4283803068 |
|---|---|
| doi | https://doi.org/10.3390/diagnostics12071619 |
| ids.doi | https://doi.org/10.3390/diagnostics12071619 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/35885524 |
| ids.openalex | https://openalex.org/W4283803068 |
| fwci | 1.17149707 |
| type | article |
| title | Comparison between Machine Learning and Multiple Linear Regression to Identify Abnormal Thallium Myocardial Perfusion Scan in Chinese Type 2 Diabetes |
| biblio.issue | 7 |
| biblio.volume | 12 |
| biblio.last_page | 1619 |
| biblio.first_page | 1619 |
| topics[0].id | https://openalex.org/T10372 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.998199999332428 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2741 |
| topics[0].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[0].display_name | Cardiac Imaging and Diagnostics |
| topics[1].id | https://openalex.org/T12422 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9907000064849854 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2741 |
| topics[1].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[1].display_name | Radiomics and Machine Learning in Medical Imaging |
| topics[2].id | https://openalex.org/T10821 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9866999983787537 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2705 |
| topics[2].subfield.display_name | Cardiology and Cardiovascular Medicine |
| topics[2].display_name | Cardiovascular Function and Risk Factors |
| is_xpac | False |
| apc_list.value | 2000 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2165 |
| apc_paid.value | 2000 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2165 |
| concepts[0].id | https://openalex.org/C2777538456 |
| concepts[0].level | 4 |
| concepts[0].score | 0.6984648108482361 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q311213 |
| concepts[0].display_name | Glycated hemoglobin |
| concepts[1].id | https://openalex.org/C71924100 |
| concepts[1].level | 0 |
| concepts[1].score | 0.653770923614502 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[1].display_name | Medicine |
| concepts[2].id | https://openalex.org/C126322002 |
| concepts[2].level | 1 |
| concepts[2].score | 0.5562310218811035 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11180 |
| concepts[2].display_name | Internal medicine |
| concepts[3].id | https://openalex.org/C164705383 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5273041725158691 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q10379 |
| concepts[3].display_name | Cardiology |
| concepts[4].id | https://openalex.org/C2780306776 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5189194679260254 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q426660 |
| concepts[4].display_name | Creatinine |
| concepts[5].id | https://openalex.org/C2778213512 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5031339526176453 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q844935 |
| concepts[5].display_name | Coronary artery disease |
| concepts[6].id | https://openalex.org/C48921125 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5014863014221191 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q10861030 |
| concepts[6].display_name | Linear regression |
| concepts[7].id | https://openalex.org/C2780221984 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4890469014644623 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q131191 |
| concepts[7].display_name | Body mass index |
| concepts[8].id | https://openalex.org/C84393581 |
| concepts[8].level | 2 |
| concepts[8].score | 0.44592395424842834 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q82642 |
| concepts[8].display_name | Blood pressure |
| concepts[9].id | https://openalex.org/C555293320 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4182042181491852 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q12206 |
| concepts[9].display_name | Diabetes mellitus |
| concepts[10].id | https://openalex.org/C2777180221 |
| concepts[10].level | 3 |
| concepts[10].score | 0.40508121252059937 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q3025883 |
| concepts[10].display_name | Type 2 diabetes |
| concepts[11].id | https://openalex.org/C33923547 |
| concepts[11].level | 0 |
| concepts[11].score | 0.2916043996810913 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[11].display_name | Mathematics |
| concepts[12].id | https://openalex.org/C105795698 |
| concepts[12].level | 1 |
| concepts[12].score | 0.18350887298583984 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[12].display_name | Statistics |
| concepts[13].id | https://openalex.org/C134018914 |
| concepts[13].level | 1 |
| concepts[13].score | 0.12547248601913452 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q162606 |
| concepts[13].display_name | Endocrinology |
| keywords[0].id | https://openalex.org/keywords/glycated-hemoglobin |
| keywords[0].score | 0.6984648108482361 |
| keywords[0].display_name | Glycated hemoglobin |
| keywords[1].id | https://openalex.org/keywords/medicine |
| keywords[1].score | 0.653770923614502 |
| keywords[1].display_name | Medicine |
| keywords[2].id | https://openalex.org/keywords/internal-medicine |
| keywords[2].score | 0.5562310218811035 |
| keywords[2].display_name | Internal medicine |
| keywords[3].id | https://openalex.org/keywords/cardiology |
| keywords[3].score | 0.5273041725158691 |
| keywords[3].display_name | Cardiology |
| keywords[4].id | https://openalex.org/keywords/creatinine |
| keywords[4].score | 0.5189194679260254 |
| keywords[4].display_name | Creatinine |
| keywords[5].id | https://openalex.org/keywords/coronary-artery-disease |
| keywords[5].score | 0.5031339526176453 |
| keywords[5].display_name | Coronary artery disease |
| keywords[6].id | https://openalex.org/keywords/linear-regression |
| keywords[6].score | 0.5014863014221191 |
| keywords[6].display_name | Linear regression |
| keywords[7].id | https://openalex.org/keywords/body-mass-index |
| keywords[7].score | 0.4890469014644623 |
| keywords[7].display_name | Body mass index |
| keywords[8].id | https://openalex.org/keywords/blood-pressure |
| keywords[8].score | 0.44592395424842834 |
| keywords[8].display_name | Blood pressure |
| keywords[9].id | https://openalex.org/keywords/diabetes-mellitus |
| keywords[9].score | 0.4182042181491852 |
| keywords[9].display_name | Diabetes mellitus |
| keywords[10].id | https://openalex.org/keywords/type-2-diabetes |
| keywords[10].score | 0.40508121252059937 |
| keywords[10].display_name | Type 2 diabetes |
| keywords[11].id | https://openalex.org/keywords/mathematics |
| keywords[11].score | 0.2916043996810913 |
| keywords[11].display_name | Mathematics |
| keywords[12].id | https://openalex.org/keywords/statistics |
| keywords[12].score | 0.18350887298583984 |
| keywords[12].display_name | Statistics |
| keywords[13].id | https://openalex.org/keywords/endocrinology |
| keywords[13].score | 0.12547248601913452 |
| keywords[13].display_name | Endocrinology |
| language | en |
| locations[0].id | doi:10.3390/diagnostics12071619 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210172076 |
| locations[0].source.issn | 2075-4418 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2075-4418 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Diagnostics |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/2075-4418/12/7/1619/pdf?version=1657176881 |
| 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 | Diagnostics |
| locations[0].landing_page_url | https://doi.org/10.3390/diagnostics12071619 |
| locations[1].id | pmid:35885524 |
| 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 | Diagnostics (Basel, Switzerland) |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/35885524 |
| locations[2].id | pmh:oai:doaj.org/article:c7a4f9a5d74340f6bdfb6b65ee609046 |
| locations[2].is_oa | True |
| 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 | cc-by-sa |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Diagnostics, Vol 12, Iss 7, p 1619 (2022) |
| locations[2].landing_page_url | https://doaj.org/article/c7a4f9a5d74340f6bdfb6b65ee609046 |
| locations[3].id | pmh:oai:mdpi.com:/2075-4418/12/7/1619/ |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S4306400947 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | True |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | MDPI (MDPI AG) |
| locations[3].source.host_organization | https://openalex.org/I4210097602 |
| locations[3].source.host_organization_name | Multidisciplinary Digital Publishing Institute (Switzerland) |
| locations[3].source.host_organization_lineage | https://openalex.org/I4210097602 |
| locations[3].license | cc-by |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/cc-by |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Diagnostics; Volume 12; Issue 7; Pages: 1619 |
| locations[3].landing_page_url | https://dx.doi.org/10.3390/diagnostics12071619 |
| locations[4].id | pmh:oai:pubmedcentral.nih.gov:9324130 |
| locations[4].is_oa | True |
| locations[4].source.id | https://openalex.org/S2764455111 |
| locations[4].source.issn | |
| locations[4].source.type | repository |
| locations[4].source.is_oa | False |
| locations[4].source.issn_l | |
| locations[4].source.is_core | False |
| locations[4].source.is_in_doaj | False |
| locations[4].source.display_name | PubMed Central |
| locations[4].source.host_organization | https://openalex.org/I1299303238 |
| locations[4].source.host_organization_name | National Institutes of Health |
| locations[4].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[4].license | other-oa |
| locations[4].pdf_url | |
| locations[4].version | submittedVersion |
| locations[4].raw_type | Text |
| locations[4].license_id | https://openalex.org/licenses/other-oa |
| locations[4].is_accepted | False |
| locations[4].is_published | False |
| locations[4].raw_source_name | Diagnostics (Basel) |
| locations[4].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/9324130 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5101728961 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-9444-8249 |
| authorships[0].author.display_name | Jiunn‐Diann Lin |
| authorships[0].countries | TW |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I47519274 |
| authorships[0].affiliations[0].raw_affiliation_string | Division of Endocrinology and Metabolism, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei City 11031, Taiwan |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I4210158385 |
| authorships[0].affiliations[1].raw_affiliation_string | Division of Endocrinology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23651, Taiwan |
| authorships[0].institutions[0].id | https://openalex.org/I47519274 |
| authorships[0].institutions[0].ror | https://ror.org/05031qk94 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I47519274 |
| authorships[0].institutions[0].country_code | TW |
| authorships[0].institutions[0].display_name | Taipei Medical University |
| authorships[0].institutions[1].id | https://openalex.org/I4210158385 |
| authorships[0].institutions[1].ror | https://ror.org/04k9dce70 |
| authorships[0].institutions[1].type | healthcare |
| authorships[0].institutions[1].lineage | https://openalex.org/I4210158385 |
| authorships[0].institutions[1].country_code | TW |
| authorships[0].institutions[1].display_name | Taipei Medical University-Shuang Ho Hospital |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Jiunn-Diann Lin |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Division of Endocrinology and Metabolism, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei City 11031, Taiwan, Division of Endocrinology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23651, Taiwan |
| authorships[1].author.id | https://openalex.org/A5112049686 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Dee Pei |
| authorships[1].countries | TW |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I114150738 |
| authorships[1].affiliations[0].raw_affiliation_string | Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen Catholic University Hospital, New Taipei 24352, Taiwan |
| authorships[1].affiliations[1].institution_ids | https://openalex.org/I114150738 |
| authorships[1].affiliations[1].raw_affiliation_string | School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei 24205, Taiwan |
| authorships[1].institutions[0].id | https://openalex.org/I114150738 |
| authorships[1].institutions[0].ror | https://ror.org/04je98850 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I114150738 |
| authorships[1].institutions[0].country_code | TW |
| authorships[1].institutions[0].display_name | Fu Jen Catholic University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Dee Pei |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen Catholic University Hospital, New Taipei 24352, Taiwan, School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei 24205, Taiwan |
| authorships[2].author.id | https://openalex.org/A5017847363 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-5590-2744 |
| authorships[2].author.display_name | Fang-Yu Chen |
| authorships[2].countries | TW |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I114150738 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei 24205, Taiwan |
| authorships[2].institutions[0].id | https://openalex.org/I114150738 |
| authorships[2].institutions[0].ror | https://ror.org/04je98850 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I114150738 |
| authorships[2].institutions[0].country_code | TW |
| authorships[2].institutions[0].display_name | Fu Jen Catholic University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Fang-Yu Chen |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei 24205, Taiwan |
| authorships[3].author.id | https://openalex.org/A5029957851 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-6118-6070 |
| authorships[3].author.display_name | Chung‐Ze Wu |
| authorships[3].countries | TW |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210158385 |
| authorships[3].affiliations[0].raw_affiliation_string | Division of Endocrinology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23651, Taiwan |
| authorships[3].affiliations[1].institution_ids | https://openalex.org/I47519274 |
| authorships[3].affiliations[1].raw_affiliation_string | Division of Endocrinology and Metabolism, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei City 11031, Taiwan |
| authorships[3].institutions[0].id | https://openalex.org/I47519274 |
| authorships[3].institutions[0].ror | https://ror.org/05031qk94 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I47519274 |
| authorships[3].institutions[0].country_code | TW |
| authorships[3].institutions[0].display_name | Taipei Medical University |
| authorships[3].institutions[1].id | https://openalex.org/I4210158385 |
| authorships[3].institutions[1].ror | https://ror.org/04k9dce70 |
| authorships[3].institutions[1].type | healthcare |
| authorships[3].institutions[1].lineage | https://openalex.org/I4210158385 |
| authorships[3].institutions[1].country_code | TW |
| authorships[3].institutions[1].display_name | Taipei Medical University-Shuang Ho Hospital |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Chung-Ze Wu |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Division of Endocrinology and Metabolism, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei City 11031, Taiwan, Division of Endocrinology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23651, Taiwan |
| authorships[4].author.id | https://openalex.org/A5044922945 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Chieh‐Hua Lu |
| authorships[4].countries | TW |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I1293718874, https://openalex.org/I68171755 |
| authorships[4].affiliations[0].raw_affiliation_string | Division of Endocrinology and Metabolism, Department of Internal Medicine, Tri-Service General Hospital, School of Medicine, National Defense Medical Center, No. 325. Sec. 2, Chenggong Rd., Neihu District, Taipei City 11490, Taiwan |
| authorships[4].institutions[0].id | https://openalex.org/I68171755 |
| authorships[4].institutions[0].ror | https://ror.org/02bn97g32 |
| authorships[4].institutions[0].type | healthcare |
| authorships[4].institutions[0].lineage | https://openalex.org/I68171755 |
| authorships[4].institutions[0].country_code | TW |
| authorships[4].institutions[0].display_name | National Defense Medical Center |
| authorships[4].institutions[1].id | https://openalex.org/I1293718874 |
| authorships[4].institutions[1].ror | https://ror.org/007h4qe29 |
| authorships[4].institutions[1].type | healthcare |
| authorships[4].institutions[1].lineage | https://openalex.org/I1293718874 |
| authorships[4].institutions[1].country_code | TW |
| authorships[4].institutions[1].display_name | Tri-Service General Hospital |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Chieh-Hua Lu |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Division of Endocrinology and Metabolism, Department of Internal Medicine, Tri-Service General Hospital, School of Medicine, National Defense Medical Center, No. 325. Sec. 2, Chenggong Rd., Neihu District, Taipei City 11490, Taiwan |
| authorships[5].author.id | https://openalex.org/A5101984035 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-0593-6428 |
| authorships[5].author.display_name | Li-Ying Huang |
| authorships[5].countries | TW |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I114150738 |
| authorships[5].affiliations[0].raw_affiliation_string | School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei 24205, Taiwan |
| authorships[5].affiliations[1].institution_ids | https://openalex.org/I114150738 |
| authorships[5].affiliations[1].raw_affiliation_string | Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen Catholic University Hospital, New Taipei 24352, Taiwan |
| authorships[5].institutions[0].id | https://openalex.org/I114150738 |
| authorships[5].institutions[0].ror | https://ror.org/04je98850 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I114150738 |
| authorships[5].institutions[0].country_code | TW |
| authorships[5].institutions[0].display_name | Fu Jen Catholic University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Li-Ying Huang |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen Catholic University Hospital, New Taipei 24352, Taiwan, School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei 24205, Taiwan |
| authorships[6].author.id | https://openalex.org/A5090547586 |
| authorships[6].author.orcid | https://orcid.org/0000-0001-7673-3567 |
| authorships[6].author.display_name | Chun‐Heng Kuo |
| authorships[6].countries | TW |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I114150738 |
| authorships[6].affiliations[0].raw_affiliation_string | School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei 24205, Taiwan |
| authorships[6].affiliations[1].institution_ids | https://openalex.org/I114150738 |
| authorships[6].affiliations[1].raw_affiliation_string | Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen Catholic University Hospital, New Taipei 24352, Taiwan |
| authorships[6].institutions[0].id | https://openalex.org/I114150738 |
| authorships[6].institutions[0].ror | https://ror.org/04je98850 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I114150738 |
| authorships[6].institutions[0].country_code | TW |
| authorships[6].institutions[0].display_name | Fu Jen Catholic University |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Chun-Heng Kuo |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen Catholic University Hospital, New Taipei 24352, Taiwan, School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei 24205, Taiwan |
| authorships[7].author.id | https://openalex.org/A5103096303 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-0051-2407 |
| authorships[7].author.display_name | Shi‐Wen Kuo |
| authorships[7].countries | TW |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I4210097004 |
| authorships[7].affiliations[0].raw_affiliation_string | Division of Endocrinology and Metabolism, Department of Internal Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 289, Jianguo Rd., Xindian Dist., New Taipei City 23142, Taiwan |
| authorships[7].institutions[0].id | https://openalex.org/I4210097004 |
| authorships[7].institutions[0].ror | https://ror.org/00q017g63 |
| authorships[7].institutions[0].type | healthcare |
| authorships[7].institutions[0].lineage | https://openalex.org/I4210097004 |
| authorships[7].institutions[0].country_code | TW |
| authorships[7].institutions[0].display_name | Taipei Tzu Chi Hospital |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Shi-Wen Kuo |
| authorships[7].is_corresponding | True |
| authorships[7].raw_affiliation_strings | Division of Endocrinology and Metabolism, Department of Internal Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 289, Jianguo Rd., Xindian Dist., New Taipei City 23142, Taiwan |
| authorships[8].author.id | https://openalex.org/A5100670013 |
| authorships[8].author.orcid | https://orcid.org/0000-0002-6381-4269 |
| authorships[8].author.display_name | Yen‐Lin Chen |
| authorships[8].countries | TW |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I1293718874, https://openalex.org/I68171755 |
| authorships[8].affiliations[0].raw_affiliation_string | Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, No. 325. Sec. 2, Chenggong Rd., Neihu District, Taipei City 11490, Taiwan |
| authorships[8].institutions[0].id | https://openalex.org/I68171755 |
| authorships[8].institutions[0].ror | https://ror.org/02bn97g32 |
| authorships[8].institutions[0].type | healthcare |
| authorships[8].institutions[0].lineage | https://openalex.org/I68171755 |
| authorships[8].institutions[0].country_code | TW |
| authorships[8].institutions[0].display_name | National Defense Medical Center |
| authorships[8].institutions[1].id | https://openalex.org/I1293718874 |
| authorships[8].institutions[1].ror | https://ror.org/007h4qe29 |
| authorships[8].institutions[1].type | healthcare |
| authorships[8].institutions[1].lineage | https://openalex.org/I1293718874 |
| authorships[8].institutions[1].country_code | TW |
| authorships[8].institutions[1].display_name | Tri-Service General Hospital |
| authorships[8].author_position | last |
| authorships[8].raw_author_name | Yen-Lin Chen |
| authorships[8].is_corresponding | True |
| authorships[8].raw_affiliation_strings | Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, No. 325. Sec. 2, Chenggong Rd., Neihu District, Taipei City 11490, Taiwan |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/2075-4418/12/7/1619/pdf?version=1657176881 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Comparison between Machine Learning and Multiple Linear Regression to Identify Abnormal Thallium Myocardial Perfusion Scan in Chinese Type 2 Diabetes |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10372 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.998199999332428 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2741 |
| primary_topic.subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| primary_topic.display_name | Cardiac Imaging and Diagnostics |
| related_works | https://openalex.org/W2089758678, https://openalex.org/W2057668392, https://openalex.org/W2422737368, https://openalex.org/W1950660371, https://openalex.org/W2317297998, https://openalex.org/W1972573225, https://openalex.org/W4319058790, https://openalex.org/W4380266103, https://openalex.org/W2087851501, https://openalex.org/W2010489983 |
| cited_by_count | 6 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 3 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 1 |
| locations_count | 5 |
| best_oa_location.id | doi:10.3390/diagnostics12071619 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210172076 |
| best_oa_location.source.issn | 2075-4418 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2075-4418 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Diagnostics |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/2075-4418/12/7/1619/pdf?version=1657176881 |
| 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 | Diagnostics |
| best_oa_location.landing_page_url | https://doi.org/10.3390/diagnostics12071619 |
| primary_location.id | doi:10.3390/diagnostics12071619 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210172076 |
| primary_location.source.issn | 2075-4418 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2075-4418 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Diagnostics |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/2075-4418/12/7/1619/pdf?version=1657176881 |
| 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 | Diagnostics |
| primary_location.landing_page_url | https://doi.org/10.3390/diagnostics12071619 |
| publication_date | 2022-07-03 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W2905951300, https://openalex.org/W4200026682, https://openalex.org/W1932640386, https://openalex.org/W1963542379, https://openalex.org/W2014759828, https://openalex.org/W2096409056, https://openalex.org/W2433275654, https://openalex.org/W2032284384, https://openalex.org/W2405699595, https://openalex.org/W6713722686, https://openalex.org/W4246329408, https://openalex.org/W2058733233, https://openalex.org/W2342311534, https://openalex.org/W1967036142, https://openalex.org/W2055386089, https://openalex.org/W4225247012, https://openalex.org/W4223442957, https://openalex.org/W4220912149, https://openalex.org/W3210513267, https://openalex.org/W4221081366, https://openalex.org/W3035389179, https://openalex.org/W2140716320, https://openalex.org/W3011484826, https://openalex.org/W2767381938, https://openalex.org/W4235668112, https://openalex.org/W1983488763, https://openalex.org/W2141673980, https://openalex.org/W2030045470, https://openalex.org/W2623169439, https://openalex.org/W3041030300, https://openalex.org/W2968473560, https://openalex.org/W3038158851, https://openalex.org/W3202162127, https://openalex.org/W3210456004, https://openalex.org/W3199035829, https://openalex.org/W3117614182, https://openalex.org/W4205614357, https://openalex.org/W2111756308, https://openalex.org/W2911964244, https://openalex.org/W2124005542, https://openalex.org/W1678356000, https://openalex.org/W2070493638, https://openalex.org/W2295598076, https://openalex.org/W2606665849, https://openalex.org/W4399585247, https://openalex.org/W4399650065, https://openalex.org/W4399570248, https://openalex.org/W6869608176, https://openalex.org/W6769764061, https://openalex.org/W2475317900, https://openalex.org/W2095740581, https://openalex.org/W2095208519, https://openalex.org/W6736334230, https://openalex.org/W2798267638, https://openalex.org/W2611192750, https://openalex.org/W3175100086, https://openalex.org/W1982876774, https://openalex.org/W2139468731, https://openalex.org/W2137476445, https://openalex.org/W2801933430, https://openalex.org/W2606750738, https://openalex.org/W2480667562, https://openalex.org/W2274390025, https://openalex.org/W2755071081, https://openalex.org/W2149681568, https://openalex.org/W2171294019, https://openalex.org/W1992283514, https://openalex.org/W2145632027, https://openalex.org/W2119543188, https://openalex.org/W2324795303, https://openalex.org/W2309497016, https://openalex.org/W2664267452, https://openalex.org/W3119940386, https://openalex.org/W4226061388, https://openalex.org/W2606128734 |
| referenced_works_count | 75 |
| abstract_inverted_index.2 | 1 |
| abstract_inverted_index.a | 7, 22 |
| abstract_inverted_index.In | 34, 94, 193 |
| abstract_inverted_index.ML | 68, 145, 154, 195, 226 |
| abstract_inverted_index.as | 129 |
| abstract_inverted_index.by | 165 |
| abstract_inverted_index.in | 28, 31, 160, 202, 230 |
| abstract_inverted_index.is | 21, 155, 196 |
| abstract_inverted_index.of | 10, 44, 144, 233 |
| abstract_inverted_index.to | 39, 96, 119, 177, 199 |
| abstract_inverted_index.we | 37, 64 |
| abstract_inverted_index.368 | 87 |
| abstract_inverted_index.528 | 90 |
| abstract_inverted_index.796 | 84 |
| abstract_inverted_index.CAD | 30 |
| abstract_inverted_index.MLR | 71, 159, 201 |
| abstract_inverted_index.Our | 134 |
| abstract_inverted_index.The | 169 |
| abstract_inverted_index.all | 138 |
| abstract_inverted_index.and | 24, 72, 77, 89, 100, 111, 189, 207, 222 |
| abstract_inverted_index.are | 146, 212, 228 |
| abstract_inverted_index.can | 65 |
| abstract_inverted_index.men | 88 |
| abstract_inverted_index.not | 197 |
| abstract_inverted_index.sum | 125 |
| abstract_inverted_index.the | 41, 62, 74, 130, 174, 178, 208 |
| abstract_inverted_index.was | 127 |
| abstract_inverted_index.(ML) | 59 |
| abstract_inverted_index.From | 61 |
| abstract_inverted_index.MLR, | 98, 150 |
| abstract_inverted_index.Type | 0 |
| abstract_inverted_index.age, | 184, 217 |
| abstract_inverted_index.also | 117 |
| abstract_inverted_index.body | 213 |
| abstract_inverted_index.four | 56, 139 |
| abstract_inverted_index.from | 173 |
| abstract_inverted_index.have | 6 |
| abstract_inverted_index.high | 8 |
| abstract_inverted_index.mass | 181, 214 |
| abstract_inverted_index.mean | 141 |
| abstract_inverted_index.more | 156 |
| abstract_inverted_index.most | 175, 209 |
| abstract_inverted_index.rank | 73 |
| abstract_inverted_index.risk | 9 |
| abstract_inverted_index.root | 140 |
| abstract_inverted_index.scan | 18 |
| abstract_inverted_index.show | 136 |
| abstract_inverted_index.than | 148, 158 |
| abstract_inverted_index.that | 137, 153 |
| abstract_inverted_index.them | 79 |
| abstract_inverted_index.this | 35 |
| abstract_inverted_index.tool | 27 |
| abstract_inverted_index.tree | 102 |
| abstract_inverted_index.used | 26, 118, 128 |
| abstract_inverted_index.were | 92, 116 |
| abstract_inverted_index.with | 55, 80, 149 |
| abstract_inverted_index.(MLR) | 54 |
| abstract_inverted_index.(RF), | 106 |
| abstract_inverted_index.(SGB) | 110 |
| abstract_inverted_index.T2DM, | 85 |
| abstract_inverted_index.blood | 191, 224 |
| abstract_inverted_index.first | 170 |
| abstract_inverted_index.kinds | 232 |
| abstract_inverted_index.least | 179 |
| abstract_inverted_index.scan) | 20 |
| abstract_inverted_index.scans | 48, 164 |
| abstract_inverted_index.score | 126 |
| abstract_inverted_index.seven | 171 |
| abstract_inverted_index.these | 231 |
| abstract_inverted_index.using | 49, 166 |
| abstract_inverted_index.which | 151 |
| abstract_inverted_index.(CAD). | 14 |
| abstract_inverted_index.(T2DM) | 4 |
| abstract_inverted_index.Stress | 124 |
| abstract_inverted_index.Th-201 | 47, 122, 163, 205 |
| abstract_inverted_index.artery | 12 |
| abstract_inverted_index.errors | 143 |
| abstract_inverted_index.forest | 105 |
| abstract_inverted_index.index, | 182, 215 |
| abstract_inverted_index.linear | 52 |
| abstract_inverted_index.random | 104 |
| abstract_inverted_index.scans, | 206 |
| abstract_inverted_index.scans. | 123 |
| abstract_inverted_index.square | 142 |
| abstract_inverted_index.study, | 36, 63 |
| abstract_inverted_index.total, | 83 |
| abstract_inverted_index.women, | 91 |
| abstract_inverted_index.(CART), | 103 |
| abstract_inverted_index.(Th-201 | 19 |
| abstract_inverted_index.analyze | 120 |
| abstract_inverted_index.compare | 40, 78 |
| abstract_inverted_index.disease | 13 |
| abstract_inverted_index.eXtreme | 112 |
| abstract_inverted_index.factors | 211 |
| abstract_inverted_index.implies | 152 |
| abstract_inverted_index.machine | 57 |
| abstract_inverted_index.methods | 227 |
| abstract_inverted_index.precise | 157 |
| abstract_inverted_index.smaller | 147 |
| abstract_inverted_index.whether | 67 |
| abstract_inverted_index.abnormal | 46, 121, 162, 204 |
| abstract_inverted_index.accuracy | 43 |
| abstract_inverted_index.addition | 95 |
| abstract_inverted_index.are:body | 180 |
| abstract_inverted_index.boosting | 109, 114 |
| abstract_inverted_index.clinical | 32, 75, 167 |
| abstract_inverted_index.coronary | 11 |
| abstract_inverted_index.diabetes | 2 |
| abstract_inverted_index.endpoint | 131 |
| abstract_inverted_index.factors, | 172 |
| abstract_inverted_index.findings | 135 |
| abstract_inverted_index.glycated | 185, 218 |
| abstract_inverted_index.gradient | 108, 113 |
| abstract_inverted_index.inferior | 198 |
| abstract_inverted_index.learning | 58 |
| abstract_inverted_index.mellitus | 3 |
| abstract_inverted_index.methods. | 60 |
| abstract_inverted_index.multiple | 51 |
| abstract_inverted_index.patients | 5 |
| abstract_inverted_index.previous | 81 |
| abstract_inverted_index.studies. | 234 |
| abstract_inverted_index.superior | 229 |
| abstract_inverted_index.systolic | 188, 221 |
| abstract_inverted_index.(XGBoost) | 115 |
| abstract_inverted_index.attempted | 38 |
| abstract_inverted_index.determine | 66 |
| abstract_inverted_index.diastolic | 190, 223 |
| abstract_inverted_index.enrolled. | 93 |
| abstract_inverted_index.important | 176, 210 |
| abstract_inverted_index.including | 86 |
| abstract_inverted_index.perfusion | 17 |
| abstract_inverted_index.pressure. | 192, 225 |
| abstract_inverted_index.settings. | 33 |
| abstract_inverted_index.surpasses | 69 |
| abstract_inverted_index.variables | 76 |
| abstract_inverted_index.(dependent | 132 |
| abstract_inverted_index.evaluating | 45 |
| abstract_inverted_index.myocardial | 16 |
| abstract_inverted_index.predicting | 203 |
| abstract_inverted_index.predictive | 42 |
| abstract_inverted_index.regression | 53, 101 |
| abstract_inverted_index.reports.In | 82 |
| abstract_inverted_index.stochastic | 107 |
| abstract_inverted_index.variable). | 133 |
| abstract_inverted_index.Creatinine, | 187 |
| abstract_inverted_index.conclusion, | 194 |
| abstract_inverted_index.creatinine, | 220 |
| abstract_inverted_index.determining | 161 |
| abstract_inverted_index.extensively | 25 |
| abstract_inverted_index.hemoglobin, | 183, 186, 216, 219 |
| abstract_inverted_index.parameters. | 168 |
| abstract_inverted_index.recognizing | 29 |
| abstract_inverted_index.traditional | 50, 70, 97, 200 |
| abstract_inverted_index.Thallium-201 | 15 |
| abstract_inverted_index.non-invasive | 23 |
| abstract_inverted_index.classification | 99 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5103096303, https://openalex.org/A5100670013 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I1293718874, https://openalex.org/I4210097004, https://openalex.org/I68171755 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.8500000238418579 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.72636431 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |