Evidence map›Paper›PMID 40502268›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2025

Building Trust in Clinical AI: A Web-Based Explainable Decision Support System for Chronic Kidney Disease.

Krishna Mridha, Ming Wang, Lijun Zhang

Abstract read
PubMed Full text at PMC
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Krishna MridhaDepartment of Population and Quantitative Health Sciences, Case Western Reserve University, School of Medicine, Cleveland, OH, USA.
Ming WangDepartment of Population and Quantitative Health Sciences, Case Western Reserve University, School of Medicine, Cleveland, OH, USA.
Lijun ZhangDepartment of Population and Quantitative Health Sciences, Case Western Reserve University, School of Medicine, Cleveland, OH, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic Kidney Disease (CKD) is a significant global public health issue, affecting over 10% of the population. Timely diagnosis is crucial for effective management. Leveraging machine learning within healthcare offers promising advancements in predictive diagnostics. We developed a Web-Based Clinical Decision Support System (CDSS) for CKD, incorporating advanced Explainable AI (XAI) methods, specifically SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations). The model employs and evaluates multiple classifiers: KNN, Random Forest, AdaBoost, XGBoost, CatBoost, and Extra Trees, to predict CKD. The effectiveness of the models is assessed by measuring their accuracy, analyzing confusion matrix statistics, and the AUC. AdaBoost achieved a 100% accuracy rate. Except for KNN, all classifiers consistently reached perfect precision and sensitivity. Additionally, we present a real-time web-based application to operationalize the model, enhancing trust and accessibility for healthcare practitioners and stakeholder.

Indexed as

Chronic Kidney DiseaseExplainable AIWeb-Based Clinical Decision Support System

Identifiers

PMID40502268
PMCPMC12150721

What OpenQuestion holds

Textmetadata
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.