A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models

Talbert, Jeffery; Dwoskin, Linda P.; Bush, Heather; Meadows, Amy Lynn; Peterson, Lars E.; Mishra, Yash R; Roggenkamp, Steven K.; Wang, Fei; Kavuluru, Ramakanth; Chen, Jin

doi:10.1109/jbhi.2023.3265920

Cited by 4 publications

(11 citation statements)

References 43 publications

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“…Data were drawn from multiple sources. Four studies used data from curated claims databases, including Optum Clinformatics Data Mart, [16][17][18] IBM MarketScan, 17,19 and PharMetrics Plus. 20 Seven studies used data from insurance or managed care databases, including Healthcare Maintenance Organization data, 21 Group Health Cooperative data, 22 Kaiser Permanente, 23,24 the Veterans Affairs Informatics and Computing Infrastructure, 25,26 and Humana Research Database.…”

Section: Data Sourcesmentioning

confidence: 99%

“…Study sample size ranged between 285 and 7,992,753 people. 19,52 Patient ages ranged from 18 to 80 years. Most studies focused on patients with pain; however, 2 studies examined individuals who underwent surgical procedures, 43,53 and another 2 investigated individuals diagnosed with OUD.…”

Section: Patient Populationmentioning

confidence: 99%

“…Most studies focused on patients with pain; however, 2 studies examined individuals who underwent surgical procedures, 43,53 and another 2 investigated individuals diagnosed with OUD. 19,27 Four…”

Section: Patient Populationmentioning

confidence: 99%

“…Five studies 9,23,42,44 limited inclusion to patients who were continuously enrolled in an insurance plan. The most common study settings were within the community [17][18][19]22,24,25,32,33,36,40,41,47,49 and in hospitals and health care systems. 21,28,35,37,42,43,46,52,54,55 Outcomes Primary outcomes included opioid overdose (17% of studies), 18,32,35,38,39,46,48 fatal opioid overdose (14.6%), 24,32,35,39,50,51 OUD (41.4%), 9,16,17,19,[21][22][23][26][27][28]…”

Section: Patient Populationmentioning

confidence: 99%

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Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose

Song,

Dandapani,

Estrada

et al. 2024

J Addict Med

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Background This systematic review summarizes the development, accuracy, quality, and clinical utility of predictive models to assess the risk of opioid use disorder (OUD), persistent opioid use, and opioid overdose. Methods In accordance with Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines, 8 electronic databases were searched for studies on predictive models and OUD, overdose, or persistent use in adults until June 25, 2023. Study selection and data extraction were completed independently by 2 reviewers. Risk of bias of included studies was assessed independently by 2 reviewers using the Prediction model Risk of Bias ASsessment Tool (PROBAST). Results The literature search yielded 3130 reports; after removing 199 duplicates, excluding 2685 studies after abstract review, and excluding 204 studies after full-text review, the final sample consisted of 41 studies that developed more than 160 predictive models. Primary outcomes included opioid overdose (31.6% of studies), OUD (41.4%), and persistent opioid use (17%). The most common modeling approach was regression modeling, and the most common predictors included age, sex, mental health diagnosis history, and substance use disorder history. Most studies reported model performance via the c statistic, ranging from 0.507 to 0.959; gradient boosting tree models and neural network models performed well in the context of their own study. One study deployed a model in real time. Risk of bias was predominantly high; concerns regarding applicability were predominantly low. Conclusions Models to predict opioid-related risks are developed using diverse data sources and predictors, with a wide and heterogenous range of accuracy metrics. There is a need for further research to improve their accuracy and implementation.

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Section: Data Sourcesmentioning

confidence: 99%

Section: Patient Populationmentioning

confidence: 99%

Section: Patient Populationmentioning

confidence: 99%

Section: Patient Populationmentioning

confidence: 99%

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Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose

Song,

Dandapani,

Estrada

et al. 2024

J Addict Med

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“…At their core, LLMs are transformer models that seek to understand relationships between data. Fouladvand et al [5,6] used a transformer model to predict opioid use disorder from multiple data sources, relying on the transformer model to extract associations within and between data sources. Notably, however, this model did not include unstructured text based data, which remains challenging to work with.…”

mentioning

confidence: 99%

The ChatGPT therapist will see you now: Navigating generative artificial intelligence's potential in addiction medicine research and patient care

Tate,

Fouladvand,

Chen

et al. 2023

Addiction

Self Cite

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Comparative Performance of ChatGPT 3.5 and GPT4 on Rhinology Standardized Board Examination Questions

Patel,

Fleischer,

Filip

et al. 2024

OTO Open

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ObjectiveAdvances in deep learning and artificial intelligence (AI) have led to the emergence of large language models (LLM) like ChatGPT from OpenAI. The study aimed to evaluate the performance of ChatGPT 3.5 and GPT4 on Otolaryngology (Rhinology) Standardized Board Examination questions in comparison to Otolaryngology residents.MethodsThis study selected all 127 rhinology standardized questions from www.boardvitals.com, a commonly used study tool by otolaryngology residents preparing for board exams. Ninety‐three text‐based questions were administered to ChatGPT 3.5 and GPT4, and their answers were compared with the average results of the question bank (used primarily by otolaryngology residents). Thirty‐four image‐based questions were provided to GPT4 and underwent the same analysis. Based on the findings of an earlier study, a pass‐fail cutoff was set at the 10th percentile.ResultsOn text‐based questions, ChatGPT 3.5 answered correctly 45.2% of the time (8th percentile) (P = .0001), while GPT4 achieved 86.0% (66th percentile) (P = .001). GPT4 answered image‐based questions correctly 64.7% of the time. Projections suggest that ChatGPT 3.5 might not pass the American Board of Otolaryngology Written Question Exam (ABOto WQE), whereas GPT4 stands a strong chance of passing.DiscussionThe older LLM, ChatGPT 3.5, is unlikely to pass the ABOto WQE. However, the advanced GPT4 model exhibits a much higher likelihood of success. This rapid progression in AI indicates its potential future role in otolaryngology education.Implications for PracticeAs AI technology rapidly advances, it may be that AI‐assisted medical education, diagnosis, and treatment planning become commonplace in the medical and surgical landscape.Level of EvidenceLevel 5.

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A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models

Abstract: doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

Cited by 4 publications

References 43 publications

Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose

Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose

The ChatGPT therapist will see you now: Navigating generative artificial intelligence's potential in addiction medicine research and patient care

Comparative Performance of ChatGPT 3.5 and GPT4 on Rhinology Standardized Board Examination Questions

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