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Clinical decision making could be supported using models that better tailor their predictions to the individual patient using the wealth of data available in electronic health records (EHRs). Here, we present a hybrid pharmacokinetic\/pharmacodynamic (PKPD)\/machine learning (ML) approach that uses predictions and individual Bayesian parameter estimates from a PKPD model to enrich an ML model built on her data. We demonstrate this approach using models developed on a large real\u2010world data set of 9121 patients treated for lymphoma, breast, or thoracic cancer. We also investigate the benefits of augmenting the training data using synthetic data simulated with the PKPD model. We find that PKPD\u2010enrichment of ML models improves prediction of grade 3\u20134 neutropenia, as measured by higher precision (61%) and recall (39%) compared to PKPD model predictions (47%, 33%) or base ML model predictions (51%, 31%). PKPD augmentation of ML models showed minor improvements in recall (44%) but not precision (56%), and data augmentation required careful tuning to control overfitting its predictions to the PKPD model. PKPD enrichment of ML shows promise for leveraging both the physiology\u2010informed predictions of PKPD and the ability of ML to learn predictor\u2010outcome relationships from large data sets to predict patient response to drugs in a clinical precision dosing context.<\/jats:p>","DOI":"10.1002\/psp4.13019","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T11:50:09Z","timestamp":1690545009000},"page":"1764-1776","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Clinical decision support for chemotherapy\u2010induced neutropenia using a hybrid pharmacodynamic\/machine learning model"],"prefix":"10.1002","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3053-1251","authenticated-orcid":false,"given":"Jasmine H.","family":"Hughes","sequence":"first","affiliation":[{"name":"Insight Rx, Inc.  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