Machine Learning for Drug Overdose Surveillance
| Autor: | William Herlands, Daniel B. Neill |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Health (social science) Computer Networks and Communications Computer science Early detection Machine Learning (stat.ML) Machine learning computer.software_genre Drug overdose 01 natural sciences Machine Learning (cs.LG) 010104 statistics & probability 03 medical and health sciences Computer Science - Computers and Society 0302 clinical medicine Statistics - Machine Learning Computers and Society (cs.CY) medicine Multiple time 030212 general & internal medicine 0101 mathematics Disease surveillance business.industry General Social Sciences Behavioral pattern Opioid overdose medicine.disease 3. Good health Computer Science - Learning Anomaly detection Artificial intelligence business computer Social Sciences (miscellaneous) |
| Popis: | We describe two recently proposed machine learning approaches for discovering emerging trends in fatal accidental drug overdoses. The Gaussian Process Subset Scan enables early detection of emerging patterns in spatio-temporal data, accounting for both the non-iid nature of the data and the fact that detecting subtle patterns requires integration of information across multiple spatial areas and multiple time steps. We apply this approach to 17 years of county-aggregated data for monthly opioid overdose deaths in the New York City metropolitan area, showing clear advantages in the utility of discovered patterns as compared to typical anomaly detection approaches. To detect and characterize emerging overdose patterns that differentially affect a subpopulation of the data, including geographic, demographic, and behavioral patterns (e.g., which combinations of drugs are involved), we apply the Multidimensional Tensor Scan to 8 years of case-level overdose data from Allegheny County, PA. We discover previously unidentified overdose patterns which reveal unusual demographic clusters, show impacts of drug legislation, and demonstrate potential for early detection and targeted intervention. These approaches to early detection of overdose patterns can inform prevention and response efforts, as well as understanding the effects of policy changes. Presented at the Data For Good Exchange 2017 |
| Databáze: | OpenAIRE |
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