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PRECISION MEDICINE

Personalizing routine lab tests with machine learning

Nature Medicine volume 27, pages 1514–1515 (2021)Cite this article

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Machine learning applied to electronic medical records can be used to create personalized lab-test reference ranges and to quantify disease risk, which will pave the way for precision medicine in clinical care.

EMRs capture clinical data on the population of patients, including demographics, diagnosis codes, medication orders and laboratory tests, which results in billions of data points on millions of patients. Even though EMR data are collected for individual patient-care purposes, there is an opportunity for de-identifying the data and, together with advanced computational approaches, leveraging it for clinical and translational research.

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Fig. 1: General workflow for modeling patient EMR data for personalized medicine.

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Authors and Affiliations

  1. Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA

    Alice Tang, Tomiko Oskotsky & Marina Sirota

  2. Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA

    Alice Tang, Tomiko Oskotsky & Marina Sirota

  3. Bioengineering Graduate Program, University of California San Francisco, San Francisco, CA, USA

    Alice Tang

Authors
  1. Alice Tang
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  2. Tomiko Oskotsky
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  3. Marina Sirota
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Correspondence to Marina Sirota.

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The authors declare no competing interests.

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Tang, A., Oskotsky, T. & Sirota, M. Personalizing routine lab tests with machine learning. Nat Med 27, 1514–1515 (2021). https://doi.org/10.1038/s41591-021-01486-4

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