Transferring clinical prediction models across hospitals and electronic health record systems

Alicia Curth*, Patrick Thoral, Wilco van den Wildenberg, Peter Bijlstra, Daan de Bruin, Paul Elbers, Mattia Fornasa

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


Recent years have seen a surge in studies developing clinical prediction models based on electronic health records (EHRs) as a result of advances in machine learning techniques and data availability. Yet, validation and implementation of such models in practice are rare, in part because EHR-based clinical prediction models are more difficult to apply to new data sets than results of classical clinical studies due to less controlled clinical environments. In this paper we propose to use the theoretical framework of domain adaptation to analyze the problem of transferring machine-learning-based clinical prediction models across different hospitals and EHR systems. Using the model of Thoral et al. [12] predicting patient-level risk of readmission and mortality after intensive care unit discharge as a case study, we discuss, apply and compare multiple domain adaptation methods. We transfer the model from the original source data set to two new target data sets. We find that, while model performance deteriorates substantially when applying a model developed for one data set to another directly, updating models with training data from the target set and using methods that explicitly model differences in data sets always improves model performance. In a simulation experiment, we show that having access to data or model parameters from another hospital can substantially reduce the amount of data required to build an accurate prediction model for a new hospital. We also show that these performance gains diminish with increasing availability of data from the target hospital.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases - International Workshops of ECML PKDD 2019, Proceedings
EditorsPeggy Cellier, Kurt Driessens
Number of pages17
ISBN (Print)9783030438227
Publication statusPublished - 1 Jan 2020
Event19th Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019 - Wurzburg, Germany
Duration: 16 Sep 201920 Sep 2019

Publication series

NameCommunications in Computer and Information Science
Volume1167 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference19th Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019

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