Predicting Individual Treatment Effects: Challenges and Opportunities for Machine Learning and Artificial Intelligence
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Date
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Science and Business Media LLC
Department of Medicine
https://doi.org/10.1007/s13218-023-00827-4
Department of Medicine
https://doi.org/10.1007/s13218-023-00827-4
Abstract
Description
<jats:title>Abstract</jats:title><jats:p>Personalized medicine seeks to identify the right treatment for the right patient at the right time. Predicting the treatment effect for an individual patient has the potential to transform treatment of patients and drastically improve patients outcomes. In this work, we illustrate the potential for ML and AI methods to yield useful predictions of individual treatment effects. Using the predicted individual treatment effects (PITE) framework which uses baseline covariates (features) to predict whether a treatment is expected to yield benefit for a given patient compared to an alternative intervention we provide an illustration of the potential of such approaches and provide a detailed discussion of opportunities for further research and open challenges when seeking to predict individual treatment effects.</jats:p>
Keywords
32 Biomedical and Clinical Sciences, 3202 Clinical Sciences, Precision Medicine, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD), Bioengineering, 4.1 Discovery and preclinical testing of markers and technologies, 4.2 Evaluation of markers and technologies, Generic health relevance, 3 Good Health and Well Being