Algorithmic Fairness in Predicting Opioid Use Disorder using Machine Learning

Abstract Background Buprenorphine is a widely used treatment option for patients with opioid use disorder (OUD). Premature discontinuation from this treatment has many negative health and societal consequences. Objective To develop and evaluate a machine learning based two-stage clinical decision-making framework for predicting which patients will discontinue OUD treatment within less than a year. The proposed framework performs such prediction in two stages: (i) at the time of initiating the treatment, and (ii) after two/three months following treatment initiation. Methods For this retrospective observational analysis, we utilized Massachusetts All Payer Claims Data (MA APCD) from the year 2013 to 2015. Study sample included 5190 patients who were commercially insured, initiated buprenorphine treatment between January and December 2014, and did not have any buprenorphine prescription at least one year prior to the date of treatment initiation in 2014. Treatment discontinuation was defined as at least two consecutive months without a prescription for buprenorphine. Six machine learning models (i.e., logistic regression, decision tree, random forest, extreme-gradient boosting, support vector machine, and artificial neural network) were tested using a five-fold cross validation on the input data. The first-stage models used patients’ demographic information. The second-stage models included information on medication adherence during the early phase of treatment based on the proportion of days covered (PDC) measure. Results A substantial percentage of patients (48.7%) who started on buprenorphine discontinued the treatment within one year. The area under receiving operating characteristic curve (C-statistic) for the first stage models varied within a range of 0.55 to 0.59. The inclusion of knowledge regarding patients’ adherence at the early treatment phase in terms of two-months and three-months PDC resulted in a statistically significant increase in the models’ discriminative power (p-value < 0.001) based on the C-statistic. We also constructed interpretable decision classification rules using the decision tree model. Conclusion Machine learning models can predict which patients are most at-risk of premature treatment discontinuation with reasonable discriminative power. The proposed machine learning framework can be used as a tool to help inform a clinical decision support system following further validation. This can potentially help prescribers allocate limited healthcare resources optimally among different groups of patients based on their vulnerability to treatment discontinuation and design personalized support systems for improving patients’ long-term adherence to OUD treatment.

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PDG36 Predicting Opioid Use Disorder Using Machine Learning Methodologies

Value in Health ◽

10.1016/j.jval.2021.04.484 ◽

2021 ◽

Vol 24 ◽

pp. S93

Author(s):

A. Pradhan ◽

T. Oates ◽

F.T. Shaya

Keyword(s):

Machine Learning ◽

Opioid Use Disorder ◽

Opioid Use

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PMH53 A Machine Learning Approach to Understanding How Patient Engagement with a Prescription Digital Therapeutic Relates to Healthcare Resource Utilization in Opioid Use Disorder

Value in Health ◽

10.1016/j.jval.2021.04.675 ◽

2021 ◽

Vol 24 ◽

pp. S137

Author(s):

H.M. Shapiro ◽

R.W. Gerwien ◽

F. Velez

Keyword(s):

Machine Learning ◽

Resource Utilization ◽

Patient Engagement ◽

Healthcare Resource ◽

Healthcare Resource Utilization ◽

Opioid Use Disorder ◽

Learning Approach ◽

Opioid Use ◽

Machine Learning Approach

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Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach

Journal of Medical Internet Research ◽

10.2196/30753 ◽

2021 ◽

Vol 23 (12) ◽

pp. e30753

Author(s):

Mai ElSherief ◽

Steven A Sumner ◽

Christopher M Jones ◽

Royal K Law ◽

Akadia Kacha-Ochana ◽

...

Keyword(s):

Machine Learning ◽

Social Media ◽

Large Scale ◽

Addiction Treatment ◽

Opioid Use Disorder ◽

Supervised Machine Learning ◽

Computational Techniques ◽

Opioid Use ◽

Web Based ◽

Health Communities

Background Expanding access to and use of medication for opioid use disorder (MOUD) is a key component of overdose prevention. An important barrier to the uptake of MOUD is exposure to inaccurate and potentially harmful health misinformation on social media or web-based forums where individuals commonly seek information. There is a significant need to devise computational techniques to describe the prevalence of web-based health misinformation related to MOUD to facilitate mitigation efforts. Objective By adopting a multidisciplinary, mixed methods strategy, this paper aims to present machine learning and natural language analysis approaches to identify the characteristics and prevalence of web-based misinformation related to MOUD to inform future prevention, treatment, and response efforts. Methods The team harnessed public social media posts and comments in the English language from Twitter (6,365,245 posts), YouTube (99,386 posts), Reddit (13,483,419 posts), and Drugs-Forum (5549 posts). Leveraging public health expert annotations on a sample of 2400 of these social media posts that were found to be semantically most similar to a variety of prevailing opioid use disorder–related myths based on representational learning, the team developed a supervised machine learning classifier. This classifier identified whether a post’s language promoted one of the leading myths challenging addiction treatment: that the use of agonist therapy for MOUD is simply replacing one drug with another. Platform-level prevalence was calculated thereafter by machine labeling all unannotated posts with the classifier and noting the proportion of myth-indicative posts over all posts. Results Our results demonstrate promise in identifying social media postings that center on treatment myths about opioid use disorder with an accuracy of 91% and an area under the curve of 0.9, including how these discussions vary across platforms in terms of prevalence and linguistic characteristics, with the lowest prevalence on web-based health communities such as Reddit and Drugs-Forum and the highest on Twitter. Specifically, the prevalence of the stated MOUD myth ranged from 0.4% on web-based health communities to 0.9% on Twitter. Conclusions This work provides one of the first large-scale assessments of a key MOUD-related myth across multiple social media platforms and highlights the feasibility and importance of ongoing assessment of health misinformation related to addiction treatment.

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Factors associated with opioid cessation: a machine learning approach

10.1101/734889 ◽

2019 ◽

Author(s):

Jiayi W. Cox ◽

Richard M. Sherva ◽

Kathryn L. Lunetta ◽

Richard Saitz ◽

Mark Kon ◽

...

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Drug Use ◽

Psychiatric Disorders ◽

Structured Interview ◽

Opioid Use Disorder ◽

Support Vector ◽

Opioid Use ◽

Factors Associated ◽

Machine Learning Approach

AbstractBackground and AimsPeople with opioid use disorder (OUD) can stop using opioids on their own, with help from groups and with treatment, but there is limited research on the factors that influence opioid cessation.MethodsWe employed multiple machine learning prediction algorithms (LASSO, random forest, deep neural network, and support vector machine) to assess factors associated with ceasing opioid use in a sample comprised of African Americans (AAs) and European Americans (EAs) who met DSM-5 criteria for mild to severe OUD. Values for several thousand demographic, alcohol and other drug use, general health, and behavioral variables, as well as diagnoses for other psychiatric disorders, were obtained for each participant from a detailed semi-structured interview.ResultsSupport vector machine models performed marginally better on average than those derived using other machine learning methods with maximum prediction accuracies of 75.4% in AAs and 79.4% in EAs. Subsequent stepwise regression analyses that considered the 83 most highly ranked variables across all methods and models identified less recent cocaine use (p<5×10−8), a shorter duration of opioid use (p<5×10−6), and older age (p<5×10−9) as the strongest independent predictors of opioid cessation. Factors related to drug use comprised about half of the significant independent predictors, with other predictors related to non-drug use behaviors, psychiatric disorders, overall health, and demographics.ConclusionsThese proof-of-concept findings provide information that can help develop strategies for improving OUD management and the methods we applied provide a framework for personalizing OUD treatment.

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