Developing and validating a machine-learning algorithm to predict opioid overdose in Medicaid beneficiaries in two US states: a prognostic modelling study

Lo-Ciganic, 2019, Evaluation of machine-learning algorithms for predicting opioid overdose risk among medicare beneficiaries with opioid prescriptions, JAMA Netw Open, 2, 10.1001/jamanetworkopen.2019.0968 Lo-Ciganic, 2020, Using machine learning to predict risk of incident opioid use disorder among fee-for-service Medicare beneficiaries: a prognostic study, PLoS One, 15, 10.1371/journal.pone.0235981 Lo-Ciganic, 2021, Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: a machine-learning approach, PLoS One, 16, 10.1371/journal.pone.0248360 Reps, 2020, Wisdom of the CROUD: development and validation of a patient-level prediction model for opioid use disorder using population-level claims data, PLoS One, 15, 10.1371/journal.pone.0228632 Hastings, 2020, Predicting high-risk opioid prescriptions before they are given, Proc Natl Acad Sci USA, 117, 1917, 10.1073/pnas.1905355117 Bossuyt, 2015, STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies, BMJ, 351 Moons, 2015, Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration, Ann Intern Med, 162, W1, 10.7326/M14-0698 White, 2009, Analytic models to identify patients at risk for prescription opioid abuse, Am J Manag Care, 15, 897 Yang, 2015, Defining risk of prescription opioid overdose: pharmacy shopping and overlapping prescriptions among long-term opioid users in Medicaid, J Pain, 16, 445, 10.1016/j.jpain.2015.01.475 Hosmer, 2000, Assessing the fit of the model, 143 Saito, 2015, The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets, PLoS One, 10, 10.1371/journal.pone.0118432 Romero-Brufau, 2015, Why the C-statistic is not informative to evaluate early warning scores and what metrics to use, Crit Care, 19, 285, 10.1186/s13054-015-0999-1 Tufféry, 2011 Steyerberg, 2010, Assessing the performance of prediction models: a framework for traditional and novel measures, Epidemiology, 21, 128, 10.1097/EDE.0b013e3181c30fb2 Chalasani, 2020, Occupational patterns of opioid-related overdose deaths among Arizona Medicaid enrollees, 2008–2017, J Gen Intern Med, 35, 2210, 10.1007/s11606-020-05684-7 Hendrick, 2012, Mammography screening: a new estimate of number needed to screen to prevent one breast cancer death, AJR Am J Roentgenol, 198, 723, 10.2214/AJR.11.7146 Roberts, 2016, Lock-in programs and the opioid epidemic: a call for evidence, Am J Public Health, 106, 1918, 10.2105/AJPH.2016.303404 Green, 2017, Assessing the accuracy of opioid overdose and poisoning codes in diagnostic information from electronic health records, claims data, and death records, Pharmacoepidemiol Drug Saf, 26, 509, 10.1002/pds.4157