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

Segal, Zvi; Radinsky, Kira; Elad, Guy; Marom, Gal; Beladev, Moran; Lewis, Maor; Ehrenberg, Bar; Gillis, Plia; Korn, Liat; Koren, Gideon

doi:10.1002/prp2.669

Cited by 24 publications

(24 citation statements)

References 15 publications

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“… 27 However, our prospective accuracy is comparable to or better than studies using hold-out validation and a similar case definition for OUD. Other studies included additional features such as lab tests and vital signs, 35 additional diagnostic phenotypes developed based on unsupervised learning, 36 and a larger quantity and broader spectrum of demographic and clinical features, 37 – 39 as well as utilizing advanced ML models such as decision trees, 37 – 39 gradient boosted trees 40 and deep neural network learning. 39 – 41 Our findings demonstrate that a ML model based on retrospective data can provide accurate predictions of individual OUD cases in a prospective sample.…”

Section: Discussionmentioning

confidence: 99%

“…It is difficult to compare our results with some of the previous studies because existing literature on ML for opioid-related risk prediction is inconsistent and includes a varied array of models that predict similar but fundamentally different outcomes such as OUD, 25,35,36,[40][41][42][43] opioid misuse, 38,[44][45][46] and opioid overdose (See Tseregounis & Henry, 2021, for a review 47 ). Opioid misuse refers to the non-medical use of prescription opioids, for example, for euphoric effect.…”

Section: Predictive Performancementioning

confidence: 99%

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Individualized Prospective Prediction of Opioid Use Disorder

et al. 2022

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Objective Opioid use disorder (OUD) is a chronic relapsing disorder with a problematic pattern of opioid use, affecting nearly 27 million people worldwide. Machine learning (ML)-based prediction of OUD may lead to early detection and intervention. However, most ML prediction studies were not based on representative data sources and prospective validations, limiting their potential to predict future new cases. In the current study, we aimed to develop and prospectively validate an ML model that could predict individual OUD cases based on representative large-scale health data. Method We present an ensemble machine-learning model trained on a cross-linked Canadian administrative health data set from 2014 to 2018 ( n = 699,164), with validation of model-predicted OUD cases on a hold-out sample from 2014 to 2018 ( n = 174,791) and prospective prediction of OUD cases on a non-overlapping sample from 2019 ( n = 316,039). We used administrative records of OUD diagnosis for each subject based on International Classification of Diseases (ICD) codes. Results With 6409 OUD cases in 2019 (mean [SD], 45.34 [14.28], 3400 males), our model prospectively predicted OUD cases at a high accuracy (balanced accuracy, 86%, sensitivity, 93%; specificity 79%). In accord with prior findings, the top risk factors for OUD in this model were opioid use indicators and a history of other substance use disorders. Conclusion Our study presents an individualized prospective prediction of OUD cases by applying ML to large administrative health datasets. Such prospective predictions based on ML would be essential for potential future clinical applications in the early detection of OUD.

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Section: Discussionmentioning

confidence: 99%

Section: Predictive Performancementioning

confidence: 99%

Individualized Prospective Prediction of Opioid Use Disorder

et al. 2022

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“…It is also one of the few models that predict a repeated, time-varying outcome (in contrast to events coded as 1-time, person-level reclassifications, such as OUD diagnosis or overdose. 36,41,42 Therefore, it is difficult to compare our model performance directly with that of other models. The study most similar to ours used urine drug data combined with basic demographic data from 139 OUD patients in a hospital-based outpatient buprenorphine program to predict relapse risk after 4 weeks of urine-validated opioid abstinence.…”

Section: Discussionmentioning

confidence: 99%

Using Machine Learning to Predict Treatment Adherence in Patients on Medication for Opioid Use Disorder

Burgess-Hull

Brooks

Epstein

et al. 2022

Journal of Addiction Medicine

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Objective: Patients receiving medication for opioid use disorder (MOUD) may continue using nonprescribed drugs or have trouble with medication adherence, and it is difficult to predict which patients will continue to do so. In this study, we develop and validate an automated risk-modeling framework to predict opioid abstinence and medication adherence at a patient's next attended appointment and evaluate the predictive performance of machine-learning algorithms versus logistic regression. Methods: Urine drug screen and attendance records from 40,005 appointments drawn from 2742 patients at a multilocation office-based MOUD program were used to train logistic regression, logistic ridge regression, and XGBoost models to predict a composite indicator of treatment adherence (opioid-negative and norbuprenorphine-positive urine, no evidence of urine adulteration) at next attended appointment.Results: The XGBoost model had similar accuracy and discriminative ability (accuracy, 88%; area under the receiver operating curve, 0.87) to the two logistic regression models (accuracy, 88%; area under the receiver operating curve, 0.87). The XGBoost model had nearly perfect calibration in independent validation data; the logistic and ridge regression models slightly overestimated adherence likelihood. Historical treatment adherence, attendance rate, and fentanyl-positive urine at current appointment were the strongest contributors to treatment adherence at next attended appointment. Discussion: There is a need for risk prediction tools to improve delivery of MOUD. This study presents an automated and portable risk-modeling framework to predict treatment adherence at each patient's next attended appointment. The XGBoost algorithm appears to provide similar classification accuracy to logistic regression models; however, XGBoost may offer improved calibration of risk estimates compared with logistic regression.

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“…Recently, though machine learning (ML) methods have been used for analysis of OUD, these works do not consider fMRI signal analysis. The authors in [6] implement the Gradient Boosting trees algorithm using a commercial claims database for the analysis of patient overdose status. The work in [7] analyzes information from electronic health records to predict opioid substance dependence.…”

Section: Description Of Purposementioning

confidence: 99%

Opioid use disorder prediction using machine learning of fMRI data

Temtam

Moeller

et al. 2023

Medical Imaging 2023: Computer-Aided Diagnosis

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According to the Centers for Disease Control and Prevention (CDC) more than 932,000 people in the US have died since 1999 from a drug overdose. Just about 75% of drug overdose deaths in 2020 involved Opioid, which suggests that the US is in an Opioid overdose epidemic. Identifying individuals likely to develop Opioid use disorder (OUD) can help public health in planning effective prevention, intervention, drug overdose and recovery policies. Further, a better understanding of prediction of overdose leading to the neurobiology of OUD may lead to new therapeutics. In recent years, very limited work has been done using statistical analysis of functional magnetic resonance imaging (fMRI) methods to analyze the neurobiology of Opioid addictions in humans. In this work, for the first time in the literature, we propose a machine learning (ML) framework to predict OUD users utilizing clinical fMRI-BOLD (Blood oxygen level dependent) signal from OUD users and healthy controls (HC). We first obtain the features and validate these with those extracted from selected brain subcortical areas identified in our previous statistical analysis of the fMRI-BOLD signal discriminating OUD subjects from that of the HC. The selected features from three representative brain areas such as default mode network (DMN), salience network (SN), and executive control network (ECN) for both OUD participants and HC subjects are then processed for OUD and HC subjects' prediction. Our leave one out cross validated results with sixty-nine OUD and HC cases show 88.40% prediction accuracies. These results suggest that the proposed techniques may be utilized to gain a greater understanding of the neurobiology of OUD leading to novel therapeutic development.

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

Abstract: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Cited by 24 publications

References 15 publications

Individualized Prospective Prediction of Opioid Use Disorder

Individualized Prospective Prediction of Opioid Use Disorder

Using Machine Learning to Predict Treatment Adherence in Patients on Medication for Opioid Use Disorder

Opioid use disorder prediction using machine learning of fMRI data

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