ArticleJournal of the American Medical Informatics Association : JAMIA
A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-specific predictions.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 2 of them syntheses that pooled it.
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Who cites it
49 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning applications for human embryo assessment using time-lapse imaging: scoping review.Frontiers in reproductive health · 2025Pooled it
- Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2017Pooled it
- Toward generalizable and interpretable machine learning models in healthcare: Insights from ICU outcome predictions.Health care management science · 2026Article
- From static to dynamic: Artificial intelligence revolution in perioperative care through multimodal data fusion and closed-loop optimization.Journal of anesthesia and translational medicine · 2025Review
- Prediction of 1p/19q state in glioma by integrated deep learning method based on MRI radiomics.BMC cancer · 2025Article
- Detecting and Remediating Harmful Data Shifts for the Responsible Deployment of Clinical AI Models.JAMA network open · 2025Article
- Data Mining Models in Prediction of Vancomycin-IntermediateMicroorganisms · 2025Article
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- Development and transfer learning of self-attention model for major adverse cardiovascular events prediction across hospitals.Scientific reports · 2024Article
- Bridging the Gap: Exploring Bronchopulmonary Dysplasia through the Lens of Biomedical Informatics.Journal of clinical medicine · 2024Review
- Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST).Journal of machine learning research : JMLR · 2024Article
- An integrated pipeline for prediction of Clostridioides difficile infection.Scientific reports · 2023Article
- Circulating proteins to predict COVID-19 severity.Scientific reports · 2023Article
- Leveraging Scheme for Cross-Study Microbiome Machine Learning Prediction and Feature Evaluations.Bioengineering (Basel, Switzerland) · 2023Article
- COMMUTE: Communication-efficient transfer learning for multi-site risk prediction.Journal of biomedical informatics · 2023Article
- Identifying and evaluating barriers for the implementation of machine learning in the intensive care unit.Communications medicine · 2022Article
- A Transfer Learning Approach to Correct the Temporal Performance Drift of Clinical Prediction Models: Retrospective Cohort Study.JMIR medical informatics · 2022Article
- Generalizability challenges of mortality risk prediction models: A retrospective analysis on a multi-center database.PLOS digital health · 2022Article
- A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma.JMIR medical informatics · 2022Article
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundData-driven risk stratification models built using data from a single hospital often have a paucity of training data. However, leveraging data from other hospitals can be challenging owing to institutional differences with patients and with data coding and capture.
objectiveTo investigate three approaches to learning hospital-specific predictions about the risk of hospital-associated infection with Clostridium difficile, and perform a comparative analysis of the value of different ways of using external data to enhance hospital-specific predictions. MATERIALS AND
methodsWe evaluated each approach on 132 853 admissions from three hospitals, varying in size and location. The first approach was a single-task approach, in which only training data from the target hospital (ie, the hospital for which the model was intended) were used. The second used only data from the other two hospitals. The third approach jointly incorporated data from all hospitals while seeking a solution in the target space.
resultsThe relative performance of the three different approaches was found to be sensitive to the hospital selected as the target. However, incorporating data from all hospitals consistently had the highest performance. DISCUSSION: The results characterize the challenges and opportunities that come with (1) using data or models from collections of hospitals without adapting them to the site at which the model will be used, and (2) using only local data to build models for small institutions or rare events.
conclusionsWe show how external data from other hospitals can be successfully and efficiently incorporated into hospital-specific models.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.