Evidence map›Paper›PMID 38361526›Full record

ArticleProceedings. IEEE International Conference on Data Mining2023

Enhancing Personalized Healthcare via Capturing Disease Severity, Interaction, and Progression.

Yanchao Tan, Zihao Zhou, Leisheng Yu, Weiming Liu, Chaochao Chen, Guofang Ma, Xiao Hu, Vicki S Hertzberg, Carl Yang

Open access · greenAbstract read
In one paragraph

Article in Proceedings. IEEE International Conference on Data Mining, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.9field-weighted citation impact, top 20% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Enhancing Semantic and Structure Modeling of Diseases for Diagnosis Prediction.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 6 institutions in 2 countries.

Yanchao TanCollege of Computer and Data Science, Fuzhou University, Fuzhou, China.
Zihao ZhouCollege of Computer and Data Science, Fuzhou University, Fuzhou, China.
Leisheng YuDepartment of Computer Science, Rice University, Houston, United States.
Weiming LiuCollege of Computer Science, Zhejiang University, Hangzhou, China.
Chaochao ChenCollege of Computer Science, Zhejiang University, Hangzhou, China.
Guofang MaSchool of Computer Science, Zhejiang Gongshang University, Hangzhou, China.
Xiao HuNell Hodgson Woodruff School of Nursing, Emory University, Atlanta, United States.
Vicki S HertzbergNell Hodgson Woodruff School of Nursing, Emory University, Atlanta, United States.
Carl YangDepartment of Computer Science, Emory University, Atlanta, United States.
Emory University · USFuzhou University · CNZhejiang University · CNRice University · USWoodruff Health Sciences Center · USZhejiang Gongshang University · CN

Funding

Understanding Diabetes Heterogeneity via Mining Multimodality Interconnected DataK25DK135913 · NIDDK · EMORY UNIVERSITY · PI Ji Carl Yang · 2023 to 2026
$687k
NIDDK NIH HHS K25 DK135913
6 · The paper itself

Abstract

Personalized diagnosis prediction based on electronic health records (EHR) of patients is a promising yet challenging task for AI in healthcare. Existing studies typically ignore the heterogeneity of diseases across different patients. For example, diabetes can have different complications across different patients (e.g., hyperlipidemia and circulatory disorder), which requires personalized diagnoses and treatments. Specifically, existing models fail to consider 1)

Identifiers

PMID38361526
PMCPMC10868667
OpenAlexW4391549761

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.