Development of a machine learning algorithm for early detection of opioid use disorder.

Pharmacology Research & Perspectives
Zvi SegalGideon Koren

Abstract

Opioid use disorder (OUD) affects an estimated 16 million people worldwide. The diagnosis of OUD is commonly delayed or missed altogether. We aimed to test the utility of machine learning in creating a prediction model and algorithm for early diagnosis of OUD. We analyzed data gathered in a commercial claim database from January 1, 2006, to December 31, 2018 of 10 million medical insurance claims from 550 000 patient records. We compiled 436 predictor candidates, divided to six feature groups - demographics, chronic conditions, diagnosis and procedures features, medication features, medical costs, and episode counts. We employed the Word2Vec algorithm and the Gradient Boosting trees algorithm for the analysis. The c-statistic for the model was 0.959, with a sensitivity of 0.85 and specificity of 0.882. Positive Predictive Value (PPV) was 0.362 and Negative Predictive Value (NPV) was 0.998. Significant differences between positive OUD- and negative OUD- controls were in the mean annual amount of opioid use days, number of overlaps in opioid prescriptions per year, mean annual opioid prescriptions, and annual benzodiazepine and muscle relaxant prescriptions. Notable differences were the count of intervertebral disc disorder-relat...Continue Reading

References

Oct 16, 2015·JAMA : the Journal of the American Medical Association·Beth HanRong Cai
Feb 7, 2018·Journal of General Internal Medicine·S L CalcaterraK L Colborn
Jun 6, 2018·Journal of Substance Abuse Treatment·Allison J OberSarah B Hunter
Feb 3, 2019·The Spine Journal : Official Journal of the North American Spine Society·Aditya V KarhadeJoseph H Schwab
Feb 7, 2019·Pharmacoepidemiology and Drug Safety·Ramin MojtabaiMark Olfson
Jan 16, 2020·Proceedings of the National Academy of Sciences of the United States of America·Justine S HastingsSarah E Inman

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Software Mentioned

Word2vec
Python
XGBoost

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