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Updated: Jan 19, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Medication Adverse Reaction, Risk Stratification (MAR²S) Model
Abstract:
BACKGROUND: A fundamental responsibility of aerospace medicine is the analysis and mitigation of the human component's risk to the aviation system. Medications are part of this risk mitigation process and are present within a multitude of work environments, including aviation. For example, during fiscal year (FY) 2013-2015, the Army Aeromedical Activity (AAMA) received 8596 medication waiver requests. During this same time period the U.S. Army Medical Department's Patient Administration Systems and Biostatistics Activity reported the organization prescribed over 187,668 prescriptions for opioids, 133,475 prescriptions for SSRIs, 116,649 prescriptions for muscle relaxants, and 71,723 prescriptions for hypnotics to its active duty soldiers in the outpatient setting.METHODS: A conceptual model to mitigate the risk of adverse reactions to medications by severity score was developed based off the methodology published by Prudhomme et al.RESULTS: The mean severity score of the 50 historically safe medications in the Army aviation community is 7346. The standard deviation of the population is 7300. The difference between safe and unsafe drugs determined by subject matter experts (SME) is highly significant when tested with the nonparametric Wilcoxon rank sum test.CONCLUSION: The visual representation of the data from this conceptual model clearly demonstrates room for improvement from current methods. Historically, utilizing SME opinion has created a system with deficiencies related to high variance, inconsistencies, and perceived ambiguity. There is need for a model addressing adverse drug reactions that has concrete strengths of transparency, simplicity, and speed of use.Cronrath CM, Klick MP, Merfeld CM, Gaydos SJ.
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