A paper co-authored by Ioana Bica, Ahmed Alaa, Craig Lambert and Mihaela van der Schaar has been published in Clinical Pharmacology & Therapeutics (editor-in-chief: Piet H. van der Graaf, PhD, PharmD).
Clinical decision making needs to be supported by evidence that treatments are beneficial to individual patients. While randomized control trials are the gold standard for testing and introducing new drugs, due to the focus on specific questions with respect to establishing efficacy and safety versus standard treatment, they do not provide a full characterization of the heterogeneity in the final intended treatment population. Conversely, real‐world observational data, such as electronic health records contain large amounts of clinical information about heterogeneous patients and their response to treatments. In this paper, we introduce the main opportunities and challenges in using observational data for training machine learning methods to estimate individualized treatment effects and make treatment recommendations. We describe the modelling choices of the state‐of‐the‐art machine learning methods for causal inference, developed for estimating treatment effects both in the cross‐section and longitudinal settings. Additionally, we highlight future research directions that could lead to achieving the full potential of leveraging electronic health records and machine learning for making individualized treatment recommendations. We also discuss how experimental data from randomized control trials and Pharmacometric and Quantitative Systems Pharmacology approaches can be used to not only improve machine learning methods, but also provide ways for validating them. These future research directions will require us to collaborate across the scientific disciplines to incorporate models based on randomized control trials and known disease processes, physiology and pharmacology into these machine learning models based on electronic health records to fully optimize the opportunity these data present.
For a full list of the van der Schaar Lab’s publications, click here.