An interpretable machine learning framework for opioid overdose surveillance from emergency medical services records.
The goal of this study is to develop and validate a lightweight, interpretable machine learning (ML) classifier to identify opioid overdoses in emergency medical services (EMS) records. We conducted a comparative assessment of three feature engineering approaches designed for use with unstructured n...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0292170&type=printable |