Evidence map›Paper›PMID 41142988›Full record

ArticleiScience2025

Enhancing healthcare analytics with prompt based probabilistic graphical model.

Yan Zhuang, Junyan Zhang, Shiyuan Liu, Bing Wei, Lei Zheng, Jianwei Gao, Zaijian Zeng, Juan Xu, Kunlun He

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Yan ZhuangMedical Innovation Research Department of PLA General Hospital, Beijing, China.
Junyan ZhangMedical Innovation Research Department of PLA General Hospital, Beijing, China.
Shiyuan LiuSchool of Information, Renmin University of China, Beijing, China.
Bing WeiDepartment of Information, Medical Supplies Center of PLA General Hospital, Beijing, China.
Lei ZhengDepartment of Information, Medical Supplies Center of PLA General Hospital, Beijing, China.
Jianwei GaoDigital Health China Technologies Co., LTD., Beijing, China.
Zaijian ZengDigital Health China Technologies Co., LTD., Beijing, China.
Juan XuDigital Health China Technologies Co., LTD., Beijing, China.
Kunlun HeMedical Innovation Research Department of PLA General Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Predicting clinical outcomes is essential for effective healthcare management. Electronic medical records (EMRs) contain rich temporal and relational structures, yet conventional models often struggle to capture these patterns with interpretability. This study proposes the prompt-based pre-trained graph model (PPGM), which combines graph neural networks with prompt learning in a two-stage framework: pre-training on patient graphs and fine-tuning with gated mechanisms for edges, nodes, and labels. By preserving the intrinsic relationships in EMRs, PPGM improves accuracy in predicting outcomes such as mortality and length of stay, while enabling transparent, interpretable reasoning. The approach enhances the integration of structured medical knowledge with machine learning, offering a scalable framework for data-driven clinical decision-making across diverse healthcare settings.

Indexed as

Artificial intelligenceHealth sciencesMachine learning

Identifiers

PMID41142988
PMCPMC12547830

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.