Phenotyping of opioid overdose cases stratified by severity using machine learning. Random forests were superior to all other methods (AUC = 0.893). Features derived from the OMOP CDM and NLP boost performance. Ordinal models were inferior to traditional classification methods.
Abstract. Objective: To develop machine learning models for classifying the severity of opioid overdose events from clinical data. Materials and methods: Opioid ...
Objective To develop machine learning models for classifying the severity of opioid overdose events from clinical data. Materials and methods Opioid ...
Machine learning for phenotyping opioid overdose events Badger J, LaRose E, Mayer J, Bashiri F, Page D, Peissig P. Journal of Biomedical Informatics ...
jbadger3/ml_4_pheno_ooe: OMOP CDM data extraction methods from
github.com › jbadger3 › ml_4_pheno_ooe
This repository contains code to accompany the paper: "Machine Learning for Phenotyping Opioid Overdose Events". Please cite this paper if you use this code as ...
Aug 8, 2022 · This diagnostic study assesses the use of natural language processing and machine learning to identify substances related to overdose deaths ...
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Nov 2, 2021 · This study demonstrates the potential for using machine learning in combination with a more traditional substantive knowledge algorithm-based ...
Feb 16, 2021 · Bibliographic details on Machine learning for phenotyping opioid overdose events.
We aimed to discover computationally-derived phenotypes of opioid-related patient presentations to the ED via clinical notes and structured electronic health ...
Mar 8, 2022 · Machine learning for phenotyping opioid overdose events. Graphical ... Phenotyping of opioid overdose cases stratified by severity using machine ...