Mortality predicting models for patients with infective endocarditis: a machine learning approach Article Swipe
Ziyang Yang
,
Qi Wang
,
Xingyan Liu
,
Haolin Li
,
Shouhong Wang
,
Danqing Yu
,
Xuebiao Wei
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.17615/7tf8-a845
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.17615/7tf8-a845
Related Topics
Concepts
No concepts available.
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.17615/7tf8-a845
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4415277536
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4415277536Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.17615/7tf8-a845Digital Object Identifier
- Title
-
Mortality predicting models for patients with infective endocarditis: a machine learning approachWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-07-10Full publication date if available
- Authors
-
Ziyang Yang, Qi Wang, Xingyan Liu, Haolin Li, Shouhong Wang, Danqing Yu, Xuebiao WeiList of authors in order
- Landing page
-
https://doi.org/10.17615/7tf8-a845Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.17615/7tf8-a845Direct OA link when available
- Cited by
-
0Total citation count in OpenAlex
Full payload
| id | https://openalex.org/W4415277536 |
|---|---|
| doi | https://doi.org/10.17615/7tf8-a845 |
| ids.doi | https://doi.org/10.17615/7tf8-a845 |
| ids.openalex | https://openalex.org/W4415277536 |
| fwci | 0.0 |
| type | article |
| title | Mortality predicting models for patients with infective endocarditis: a machine learning approach |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11775 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9228000044822693 |
| 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 | COVID-19 diagnosis using AI |
| 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 | 0.9179999828338623 |
| 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 | |
| apc_paid | |
| language | en |
| locations[0].id | doi:10.17615/7tf8-a845 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S7407051488 |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | UNC Libraries |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | |
| locations[0].raw_type | article-journal |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.17615/7tf8-a845 |
| indexed_in | datacite |
| authorships[0].author.id | https://openalex.org/A5018314602 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-6605-3987 |
| authorships[0].author.display_name | Ziyang Yang |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Zi-yang, Yang |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5100341303 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-5287-6050 |
| authorships[1].author.display_name | Qi Wang |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Qi, Wang |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5050215381 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Xingyan Liu |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Liu, Xingyan |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5109778039 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Haolin Li |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Li, Haolin |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5109412487 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-2624-6784 |
| authorships[4].author.display_name | Shouhong Wang |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Wang, Shouhong |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5060562607 |
| authorships[5].author.orcid | https://orcid.org/0000-0003-0785-3965 |
| authorships[5].author.display_name | Danqing Yu |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Yu, Danqing |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5074464132 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-1279-9441 |
| authorships[6].author.display_name | Xuebiao Wei |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Wei, Xuebiao |
| authorships[6].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.17615/7tf8-a845 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-17T00:00:00 |
| display_name | Mortality predicting models for patients with infective endocarditis: a machine learning approach |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T11775 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9228000044822693 |
| 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 | COVID-19 diagnosis using AI |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.17615/7tf8-a845 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S7407051488 |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | UNC Libraries |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
| best_oa_location.version | |
| best_oa_location.raw_type | article-journal |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.17615/7tf8-a845 |
| primary_location.id | doi:10.17615/7tf8-a845 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S7407051488 |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | UNC Libraries |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | |
| primary_location.raw_type | article-journal |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.17615/7tf8-a845 |
| publication_date | 2025-07-10 |
| publication_year | 2025 |
| referenced_works_count | 0 |
| abstract_inverted_index | |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.65655256 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |