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

Ioannis N. Anastopoulos, Chloe K. Herczeg, Kasey N. Davis and Atray C. Dixit
Additional contact information
Ioannis N. Anastopoulos: Biomolecular Engineering, University of California, Santa Cruz, CA 95064, 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

IJERPH, 2021, vol. 18, issue 5, 1-11

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.

Keywords: adverse events; real world evidence; neural networks; graph convolution; FDA FAERS; UK Biobank (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/18/5/2600/pdf (application/pdf)
https://www.mdpi.com/1660-4601/18/5/2600/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:18:y:2021:i:5:p:2600-:d:511110

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().