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Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events

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  • Ioannis N. Anastopoulos

    (Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA
    Coral Genomics, Inc., 953 Indiana St., San Francisco, CA 94107, USA)

  • Chloe K. Herczeg

    (Coral Genomics, Inc., 953 Indiana St., San Francisco, CA 94107, USA)

  • Kasey N. Davis

    (Coral Genomics, Inc., 953 Indiana St., San Francisco, CA 94107, USA)

  • Atray C. Dixit

    (Coral Genomics, Inc., 953 Indiana St., San Francisco, CA 94107, USA)

Abstract

While the clinical approval process is able to filter out medications whose utility does not offset their adverse drug reaction profile in humans, it is not well suited to characterizing lower frequency issues and idiosyncratic multi-drug interactions that can happen in real world diverse patient populations. With a growing abundance of real-world evidence databases containing hundreds of thousands of patient records, it is now feasible to build machine learning models that incorporate individual patient information to provide personalized adverse event predictions. In this study, we build models that integrate patient specific demographic, clinical, and genetic features (when available) with drug structure to predict adverse drug reactions. We develop an extensible graph convolutional approach to be able to integrate molecular effects from the variable number of medications a typical patient may be taking. Our model outperforms standard machine learning methods at the tasks of predicting hospitalization and death in the UK Biobank dataset yielding an R 2 of 0.37 and an AUC of 0.90, respectively. We believe our model has potential for evaluating new therapeutic compounds for individualized toxicities in real world diverse populations. It can also be used to prioritize medications when there are multiple options being considered for treatment.

Suggested Citation

  • Ioannis N. Anastopoulos & Chloe K. Herczeg & Kasey N. Davis & Atray C. Dixit, 2021. "Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events," IJERPH, MDPI, vol. 18(5), pages 1-11, March.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:5:p:2600-:d:511110
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    References listed on IDEAS

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    1. Clare Bycroft & Colin Freeman & Desislava Petkova & Gavin Band & Lloyd T. Elliott & Kevin Sharp & Allan Motyer & Damjan Vukcevic & Olivier Delaneau & Jared O’Connell & Adrian Cortes & Samantha Welsh &, 2018. "The UK Biobank resource with deep phenotyping and genomic data," Nature, Nature, vol. 562(7726), pages 203-209, October.
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    Cited by:

    1. Yanli Zhang & Xinmiao Li & Yu Yang & Tao Wang, 2022. "Disease- and Drug-Related Knowledge Extraction for Health Management from Online Health Communities Based on BERT-BiGRU-ATT," IJERPH, MDPI, vol. 19(24), pages 1-13, December.

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