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Developing and validating a sequence-aware deep learning model for infection risk prediction in home care.

Zidu Xu¹, Shuang Zhou², Jiyoun Song³

¹School of Nursing, Columbia University, New York, NY 10032, USA.

International Journal of Medical Informatics

|May 22, 2026

Summary

A new deep learning model accurately predicts infection-related hospitalizations in home care patients by analyzing electronic health records and clinical notes. This tool helps identify high-risk individuals for early intervention.

Keywords:

Acute care utilization Deep learning Home care Infection risk prediction Natural language processing Risk stratification

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Area of Science:

Artificial Intelligence in Healthcare
Clinical Informatics
Predictive Analytics

Background:

Infections are a major cause of hospitalizations and emergency department (ED) visits for home care patients.
Current prediction tools often fail to fully leverage longitudinal data from electronic health records (EHR) and clinical notes.

Purpose of the Study:

To evaluate a sequence-aware deep learning approach for predicting infection-related hospitalizations or ED visits.
To integrate structured EHR data with Natural Language Processing (NLP)-derived features from clinical notes.
To develop and assess a three-tier risk stratification tool for home care patients.

Main Methods:

Retrospective cohort study of 23,321 home care episodes (2015-2017).
Compared sequence-aware models with a non-sequential baseline using structured and NLP-derived features.
Evaluated model performance using AUROC, AUPRC, and F1-score for predicting infection-related events within 2- to 4-day windows.

Main Results:

Sequence-aware models, especially with NLP features, significantly outperformed the baseline.
The best model achieved an AUROC of 0.991, AUPRC of 0.885, and F1-score of 0.774.
A three-tier stratification tool identified 77.7% of infection events in the highest-risk 5% of episodes, with equitable performance across subgroups.

Conclusions:

Sequence-aware deep learning effectively predicts near-term infection risks in home care.
The integrated approach using EHR and NLP data offers accurate and equitable risk prediction.
The developed tool can aid in targeted triage and early intervention to reduce acute care utilization.