From Data to Decision: Intelligent Construction and Application of Hospital Clinical Research Models

Corresponding author: Zhu Weiguo, zhuwg@pumch.cn

DOI: 10.12201/bmr.202604.00035

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  Abstract: Purpose/Significance To build a closed-loop Intelligent modeling platform encompassing “dataset construction–data processing–AI modeling–clinical validation,” achieving standardized and visualized workflows from data to decision-making, and promoting the transformation of hospital clinical research toward evidence-based and precision practices. Method/Process Integrate multi-source heterogeneous clinical data from hospitals, combine traditional machine learning and deep learning algorithms with explainable AI techniques, and complement the system with a knowledge traceability verification mechanism and a full-process management framework. Result/Conclusion The platform has demonstrated remarkable effectiveness in clinical research practical applications and shortened the modeling cycle from 2 months to 2 weeks. The risk prediction model for acute high-risk chest pain achieves an accuracy of 87.1% (AUC=0.906), and the mean absolute error (MAE) for heparin dosage adjustment prediction is 0.8612. To date, it has provided nearly 10,000 clinical calls. This platform effectively addresses the challenge of integrating the entire clinical research process, adapting to the needs of clinical research and providing a practical paradigm for the development of trustworthy clinical research tools. Future efforts should focus on expanding knowledge sources coverage, enhancing multi-modal data fusion capabilities, etc.

  Key words: Clinical research; Intelligent modeling platform; machine learning; explainable AI; clinical decision support

  Submit time: 7 April 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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  1	2025-12-25	10.12201/bmr.202604.00035V1	Download

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