Autoencoder-Based Representation Learning for Similar Patients Retrieval From Electronic Health Records: Comparative Study
This study provides a comprehensive analysis of the performance of different AE variants in retrieving similar patients and evaluates the impact of various hyperparameter configurations on model performance. The findings lay the groundwork for future development of AE-based patient similarity estimation and personalized medicine.
Authors
- Mei Liu (ORCID: https://orcid.org/0000-0002-8036-2110)
- Deyi Li
- Bradley Taylor (ORCID: https://orcid.org/0000-0002-6414-4172)
- Shukla Aditi
- Sravani Chandaka
- Jie Xu (ORCID: https://orcid.org/0000-0001-5291-5198)
Institutions
- Florida Museum of Natural History (US)
- Medical College of Wisconsin (US)
- University of Florida Health (US)
- University of the Arts (US)
- University of Kansas Medical Center (US)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-07-24
- DOI
- https://doi.org/10.2196/68830
- Citations
- 1
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.48