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Predicting patient outcome is an important task in medical decision making, as clinician expectations of outcome drive testing and treatment decisions. Accurate models can assist clinicians by capitalizing on information from a broad spectrum of features to predict outcome. In an article in this journal, ‘A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-specific predictions,’ Wiens, Guttag, and Horvitz1 explore the use of transfer learning for improving a predictive model of Clostridium difficile infection (CDI). Their discussion focuses on the need to aggregate data for studying rare diseases but notes the failure of global models to predict accurately for specific institutions. Transfer learning attempts to rectify the generalizability problem by applying evidence from multiple sources on a related target task. Their work demonstrates how transfer learning can be utilized to create a ‘source+target’ model matching or outperforming models trained with source or target data alone. A number of important considerations when pooling data are raised by the authors. Here, we note the need for further discussion by revisiting two points raised in their paper affecting transfer: (1) feature similarity and (2) feature selection. We briefly discuss limitations of transfer learning tied to a lack of causal knowledge and posit that causal information can complement transfer learning to improve model generalization.Weins et al combined datasets by comparing the overlap of features in data collected across hospitals. However, it is important to note that overlapping features do not guarantee feature similarity. This issue was explored in the section, ‘Not all transfer is created equal’; hospital …

Transfer and transport: incorporating causal methods for improving predictive models