Peer-Reviewed Publication
Int J Med Inform2026;217106485.September 1, 2026Journal Article
Developing and validating a sequence-aware deep learning model for infection risk prediction in home care.
Zidu Xu1, Shuang Zhou2, Jiyoun Song3, David Russell4, Maryam Zolnoori5, Jingjing Shang6, Maxim Topaz7
1School of Nursing, Columbia University, New York, NY 10032, USA. Electronic address: zixu2@bwh.harvard.edu.
2Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN 55455, USA.
3Department of Biobehavioral Health Sciences, University of Pennsylvania School of Nursing, Philadelphia, PA 19104, USA.
4Department of Sociology, Appalachian State University, Boone, NC 28608, USA; Center for Home Care Policy & Research, VNS Health, New York, NY 10017, USA.
5School of Nursing, Columbia University, New York, NY 10032, USA; Center for Home Care Policy & Research, VNS Health, New York, NY 10017, USA; Columbia University Irving Medical Center, New York, NY 10032, USA.
6School of Nursing, Columbia University, New York, NY 10032, USA.
7School of Nursing, Columbia University, New York, NY 10032, USA; Center for Home Care Policy & Research, VNS Health, New York, NY 10017, USA; Data Science Institute, Columbia University, New York, NY 10027, USA.
Abstract
BACKGROUND: Infections are a leading cause of hospitalizations and emergency department (ED) visits in home care. Existing prediction tools often underutilize longitudinal patterns of home care electronic health record (EHR) and narrative clinical notes.
OBJECTIVES: To evaluate a sequence-aware deep learning approach that integrates longitudinal structured EHR data with Natural Language Processin…
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