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Mar 22, 2019 · Our overall hypothesis was that a machine-learning algorithm would perform better in predicting opioid overdose risk compared with traditional statistical approaches. The objective of this study was to develop and validate a machine-learning algorithm to predict opioid overdose among Medicare beneficiaries with at least 1 opioid prescription.
- Wei Hsuan Lo-Ciganic, James L. Huang, Hao H. Zhang, Jeremy C. Weiss, Yonghui Wu, C. Kent Kwoh, Julie...
- 2019
Dec 15, 2021 · 2.4. Implementation of machine learning algorithms to predict the risk of opioid use disorder. We investigated and evaluated the performance of several well-known machine learning algorithms to classify the individuals at higher risk for opioid use disorder. We framed this problem as a binary classification problem.
- Mahmudul Hasan, Gary J. Young, Mehul Rakeshkumar Patel, Alicia Sasser Modestino, Leon D. Sanchez, Md...
- 2021
Mar 22, 2019 · Our overall hypothesis was that a machine-learning algorithm would perform better in predicting opioid overdose risk compared with traditional statistical approaches. The objective of this study was to develop and validate a machine-learning algorithm to predict opioid overdose among Medicare beneficiaries with at least 1 opioid prescription.
- Wei Hsuan Lo-Ciganic, James L. Huang, Hao H. Zhang, Jeremy C. Weiss, Yonghui Wu, C. Kent Kwoh, Julie...
- 10.1001/jamanetworkopen.2019.0968
- 2019
- JAMA Netw Open. 2019 Mar; 2(3): e190968.
A machine-learning algorithm predicting opioid overdose derived from Pennsylvania Medicaid data performed well in external validation with more recent Pennsylvania data and with Arizona Medicaid data. The algorithm might be valuable for overdose risk prediction and stratification in Medicaid beneficiaries.
Little is known about whether machine-learning algorithms developed to predict opioid overdose using earlier years and from a single state will perform as well when applied to other populations. We aimed to develop a machine-learning algorithm to predict 3-month risk of opioid overdose using Pennsylvania Medicaid data and
Apr 5, 2018 · A need exists to accurately estimate overdose risk and improve understanding of how to deliver treatments and interventions in people with opioid use disorder in a way that reduces such risk. We consider opportunities for predictive analytics and routinely collected administrative data to evaluate how overdose could be reduced among people with opioid use disorder. Specifically, we summarise ...
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Can a machine-learning algorithm predict opioid overdose risk?
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Can machine learning predict opioid overdose among Medicare beneficiaries?
Are machine learning algorithms effective to classify patients with opioid use disorder?
Can data mining and machine learning predict opioid use disorder?
Can machine learning solve the opioid epidemic?
Mar 1, 2019 · Importance: Current approaches to identifying individuals at high risk for opioid overdose target many patients who are not truly at high risk. Objective: To develop and validate a machine-learning algorithm to predict opioid overdose risk among Medicare beneficiaries with at least 1 opioid prescription.
