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Updated: Sep 24, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

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Machine Learning-Based Prediction Models for Different Clinical Risks in Different Hospitals: Evaluation of Live

Hong Sun¹, Kristof Depraetere¹, Laurent Meesseman¹

¹Dedalus Healthcare, Antwerp, Belgium.

Journal of Medical Internet Research

|May 3, 2022

Summary

Clinical risk prediction models performed similarly in live workflows as with retrospective data. However, cross-hospital performance significantly dropped, highlighting the need for hospital-specific model calibration.

Keywords:

EHR acute kidney injury algorithm clinical risk prediction delirium electronic health record kidney live clinical workflow machine learning model model evaluation prediction risk scalability sepsis workflow

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Area of Science:

Clinical informatics
Machine learning in healthcare
Predictive modeling

Background:

Machine learning models are widely used for predicting clinical risk events.
Most models are evaluated retrospectively, with limited real-world clinical workflow and multi-hospital performance data.
This study evaluates clinical risk prediction models in live clinical workflows across three hospitals.

Purpose of the Study:

To evaluate clinical risk prediction models in live clinical workflows.
To compare their performance in live settings versus retrospective data.
To assess generalizability across three different hospitals and use cases.

Main Methods:

Trained deep learning Transformer models for delirium, sepsis, and acute kidney injury prediction using retrospective data from three hospitals.
Deployed models in live clinical workflows for daily practice, logging predictions against discharge diagnoses.
Compared live performance with retrospective evaluations and conducted cross-hospital performance assessments.

Main Results:

Model performance in live workflows closely matched retrospective data, with a slight AUROC decrease of 0.6%.
Cross-hospital evaluations showed a significant performance drop (average AUROC decrease of 8%), emphasizing the need for local calibration.
Performance degradation in cross-hospital tests indicates limitations of generic models.

Conclusions:

Hospital-specific calibration of prediction models ensures good performance in live clinical settings.
Generic machine learning models face limitations in multi-hospital deployment due to performance degradation.
A standardized development process to create specialized models for each hospital is crucial for reliable performance across institutions.