ArticleReviews in cardiovascular medicine2025
AI-based Assessment of Risk Factors for Coronary Heart Disease in Patients With Diabetes Mellitus and Construction of a Prediction Model for a Treatment Regimen.
Article in Reviews in cardiovascular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study aimed to construct a prediction model for a treatment plan for patients with coronary artery disease combined with diabetes mellitus using machine learning to efficiently formulate the treatment plan for special patients and improve the prognosis of patients, provide an explanation of the model based on SHapley Additive exPlanation (SHAP), explore the related risk factors, provide a reference for the clinic, and concurrently, to lay the foundation for the establishment of a multicenter prediction model for future treatment plans. Methods: To investigate the relationship between concomitant coronary heart disease (CHD) and diabetes mellitus (DM), this study retrospectively included patients who attended the Beijing Anzhen Hospital of Capital Medical University between 2022 and 2023. The processed data were then input into five different algorithms for model construction. The performance of each model was rigorously evaluated using five specific evaluation indicators. The SHAP algorithm also provided clear explanations and visualizations of the model's predictions. Results: The optimal set of characteristics determined by the least absolute shrinkage and selection operator (LASSO) regression were 15 features of general information, laboratory test results, and echocardiographic findings. The best model identified was the eXtreme Gradient Boost (XGBoost) model. The interpretation of the model based on the SHAP algorithm suggests that the feature in the XGBoost model that has the greatest impact on the prediction of the results is the glycated hemoglobin level. Conclusions: Using machine-learning algorithms, we built a prediction model of a treatment plan for patients with concomitant DM and CHD by integrating patients' information and screened the best feature set containing 15 features, which provides help and strategies to develop the best treatment plan for patients with concomitant DM and CHD.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.