'Black Box' to 'Conversational' Machine Learning: Ondansetron Reduces Risk of Hospital-Acquired Venous Thromboembolism

'Black Box' to 'Conversational' Machine Learning: Ondansetron Reduces Risk of Hospital-Acquired Venous Thromboembolism

Machine learning, combined with a proliferation of electronic healthcare records (EHR), has the potential to transform medicine by identifying previously unknown interventions that reduce the risk of adverse outcomes. To realize this potential, machine learning must leave the conceptual 'black...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics Vol. 25; no. 6; pp. 2204 - 2214
Main Authors: Datta, Arghya, Matlock, Matthew K., Le Dang, Na, Moulin, Thiago, Woeltje, Keith F., Yanik, Elizabeth L., Joshua Swamidass, Sanjay
Format: Journal Article
Language:English
Published: Piscataway IEEE 01-06-2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Anesthesia
Antiemetics
Aspirin
Biological system modeling
Case studies
Chemotherapy
clinical informatics
Domains
Drugs
Electronic health records
electronic healthcare records
Learning algorithms
Logistics
Machine learning
Modelling
Morbidity
Nausea
prediction models
Predictive models
Risk management
Side effects
Thromboembolism
Venous thromboembolism
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Summary:Machine learning, combined with a proliferation of electronic healthcare records (EHR), has the potential to transform medicine by identifying previously unknown interventions that reduce the risk of adverse outcomes. To realize this potential, machine learning must leave the conceptual 'black box' in complex domains to overcome several pitfalls, like the presence of confounding variables. These variables predict outcomes but are not causal, often yielding uninformative models. In this work, we envision a 'conversational' approach to design machine learning models, which couple modeling decisions to domain expertise. We demonstrate this approach via a retrospective cohort study to identify factors which affect the risk of hospital-acquired venous thromboembolism (HA-VTE). Using logistic regression for modeling, we have identified drugs that reduce the risk of HA-VTE. Our analysis reveals that ondansetron, an anti-nausea and anti-emetic medication, commonly used in treating side-effects of chemotherapy and post-general anesthesia period, substantially reduces the risk of HA-VTE when compared to aspirin (11% vs. 15% relative risk reduction or RRR, respectively). The low cost and low morbidity of ondansetron may justify further inquiry into its use as a preventative agent for HA-VTE. This case study highlights the importance of engaging domain expertise while applying machine learning in complex domains.
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ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2020.3033405