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Hayes CJ, Cucciare MA, Martin BC, Hudson TJ, Bush K, Lo-Ciganic W, Yu H, Charron E, Gordon AJ. Using data science to improve outcomes for persons with opioid use disorder. Subst Abus 2022; 43:956-963. [PMID: 35420927 PMCID: PMC9705076 DOI: 10.1080/08897077.2022.2060446] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Medication treatment for opioid use disorder (MOUD) is an effective evidence-based therapy for decreasing opioid-related adverse outcomes. Effective strategies for retaining persons on MOUD, an essential step to improving outcomes, are needed as roughly half of all persons initiating MOUD discontinue within a year. Data science may be valuable and promising for improving MOUD retention by using "big data" (e.g., electronic health record data, claims data mobile/sensor data, social media data) and specific machine learning techniques (e.g., predictive modeling, natural language processing, reinforcement learning) to individualize patient care. Maximizing the utility of data science to improve MOUD retention requires a three-pronged approach: (1) increasing funding for data science research for OUD, (2) integrating data from multiple sources including treatment for OUD and general medical care as well as data not specific to medical care (e.g., mobile, sensor, and social media data), and (3) applying multiple data science approaches with integrated big data to provide insights and optimize advances in the OUD and overall addiction fields.
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Affiliation(s)
- Corey J Hayes
- Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
- Central Arkansas Veterans Healthcare System, Center for Mental Healthcare and Outcomes Research, North Little Rock, Arkansas, USA
| | - Michael A Cucciare
- Central Arkansas Veterans Healthcare System, Center for Mental Healthcare and Outcomes Research, North Little Rock, Arkansas, USA
- Center for Health Services Research, Department of Psychiatry, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
- Veterans Affairs South Central Mental Illness Research, Education and Clinical Center, Central Arkansas Veterans Healthcare System, North Little Rock, Arkansas, USA
| | - Bradley C Martin
- Division of Pharmaceutical Evaluation and Policy, College of Pharmacy, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Teresa J Hudson
- Central Arkansas Veterans Healthcare System, Center for Mental Healthcare and Outcomes Research, North Little Rock, Arkansas, USA
- Center for Health Services Research, Department of Psychiatry, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Keith Bush
- Brain Imaging Research Center, Department of Psychiatry, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Weihsuan Lo-Ciganic
- Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA
- Center for Drug Evaluation and Safety (CoDES), College of Pharmacy, University of Florida, Gainesville, Florida, USA
| | - Hong Yu
- Department of Computer Science, Kennedy College of Sciences, University of Massachusetts Lowell, Lowell, Florida, USA
- Center for Healthcare Organization and Implementation Research, VA Bedford Healthcare System, Bedford, MA
| | - Elizabeth Charron
- Program for Addiction Research, Clinical Care, Knowledge, and Advocacy (PARCKA), Division of Epidemiology, Department of Medicine, School of Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Adam J Gordon
- Program for Addiction Research, Clinical Care, Knowledge, and Advocacy (PARCKA), Division of Epidemiology, Department of Medicine, School of Medicine, University of Utah, Salt Lake City, Utah, USA
- Informatics, Decision-Enhancement and Analytic Sciences (IDEAS) Center, VA Salt Lake City Healthcare System, Salt Lake City, Utah, USA
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Generating Accurate Electronic Health Assessment from Medical Graph. PROCEEDINGS OF THE CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING. CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING 2021; 2020:3764-3773. [PMID: 33491009 DOI: 10.18653/v1/2020.findings-emnlp.336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
One of the fundamental goals of artificial intelligence is to build computer-based expert systems. Inferring clinical diagnoses to generate a clinical assessment during a patient encounter is a crucial step towards building a medical diagnostic system. Previous works were mainly based on either medical domain-specific knowledge, or patients' prior diagnoses and clinical encounters. In this paper, we propose a novel model for automated clinical assessment generation (MCAG). MCAG is built on an innovative graph neural network, where rich clinical knowledge is incorporated into an end-to-end corpus-learning system. Our evaluation results against physician generated gold standard show that MCAG significantly improves the BLEU and rouge score compared with competitive baseline models. Further, physicians' evaluation showed that MCAG could generate high-quality assessments.
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