Machine learning for phenotyping opioid overdose events

Badger, Jonathan C.; LaRose, Eric; Mayer, John; Bashiri, Fereshteh S.; Page, David; Peissig, Peggy L.

doi:10.1016/j.jbi.2019.103185

Cited by 27 publications

(26 citation statements)

References 31 publications

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“…Regression models and random forest classifiers were used to evaluate the importance of features on different tasks. 24 , 34 , 38 , 45 Feller et al 32 used chi-squared goodness of fit tests to perform feature selection for housing status, sexual history, substance use, alcohol use, sexual orientation, and gender documentation identification.…”

Section: Resultsmentioning

confidence: 99%

Extracting social determinants of health from electronic health records using natural language processing: a systematic review

Patra

Sharma

Vekaria

et al. 2021

Journal of the American Medical Informatics Association

128

101

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Objective Social determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs. Materials and Methods A broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review. Results Smoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9). Conclusion NLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.

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Section: Resultsmentioning

confidence: 99%

Extracting social determinants of health from electronic health records using natural language processing: a systematic review

Patra

Sharma

Vekaria

et al. 2021

Journal of the American Medical Informatics Association

128

101

View full text Add to dashboard Cite

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“…Machine learning-based predictive algorithms have been touted as the new frontier of healthcare [5,19]. Random forest has emerged as a popular methodology due to its ability to work with a mixture of data types, handle missing data, and achieve high predictive performance [2,4,12,17,27,32,33]. Yet, these models are often only evaluated on discrimination, or how well the model differentiates between high risk and low risk of the event, and fail to provide any analysis of calibration.…”

Section: Introductionmentioning

confidence: 99%

CaliForest

Park

2020

Proceedings of the ACM Conference on Health, Inference, and Learning

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Real-world predictive models in healthcare should be evaluated in terms of discrimination, the ability to differentiate between high and low risk events, and calibration, or the accuracy of the risk estimates. Unfortunately, calibration is often neglected and only discrimination is analyzed. Calibration is crucial for personalized medicine as they play an increasing role in the decision making process. Since random forest is a popular model for many healthcare applications, we propose CaliForest, a new calibrated random forest. Unlike existing calibration methodologies, CaliForest utilizes the out-of-bag samples to avoid the explicit construction of a calibration set. We evaluated CaliForest on two risk prediction tasks obtained from the publicly-available MIMIC-III database. Evaluation on these binary prediction tasks demonstrates that CaliForest can achieve the same discriminative power as random forest while obtaining a better-calibrated model evaluated across six different metrics. CaliForest is published on the standard Python software repository and the code is openly available on Github. CCS CONCEPTS• Applied computing → Health informatics; • General and reference → Empirical studies; • Computing methodologies → Classification and regression trees; Bagging.

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“…5 However, recent endeavors have successfully used natural language processing (NLP)-a method of engaging computer assistance for language interpretation-combined with machine learning (ML) to develop case definitions for opioid overdose and related conditions using health data, including EMS. [13][14][15] We attempted to create an improved case definition by combining NLP-ML with a rule-based algorithm that was informed by expert personnel, including a nurse (J.S.) and a paramedic (D.E.)…”

mentioning

confidence: 99%

Characterizing Opioid Overdoses Using Emergency Medical Services Data

et al. 2021

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Objectives Tracking nonfatal overdoses in the escalating opioid overdose epidemic is important but challenging. The objective of this study was to create an innovative case definition of opioid overdose in North Carolina emergency medical services (EMS) data, with flexible methodology for application to other states’ data. Methods This study used de-identified North Carolina EMS encounter data from 2010-2015 for patients aged >12 years to develop a case definition of opioid overdose using an expert knowledge, rule-based algorithm reflecting whether key variables identified drug use/poisoning or overdose or whether the patient received naloxone. We text mined EMS narratives and applied a machine-learning classification tree model to the text to predict cases of opioid overdose. We trained models on the basis of whether the chief concern identified opioid overdose. Results Using a random sample from the data, we found the positive predictive value of this case definition to be 90.0%, as compared with 82.7% using a previously published case definition. Using our case definition, the number of unresponsive opioid overdoses increased from 3412 in 2010 to 7194 in 2015. The corresponding monthly rate increased by a factor of 1.7 from January 2010 (3.0 per 1000 encounters; n = 261 encounters) to December 2015 (5.1 per 1000 encounters; n = 622 encounters). Among EMS responses for unresponsive opioid overdose, the prevalence of naloxone use was 83%. Conclusions This study demonstrates the potential for using machine learning in combination with a more traditional substantive knowledge algorithm-based approach to create a case definition for opioid overdose in EMS data.

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Machine learning for phenotyping opioid overdose events

Cited by 27 publications

References 31 publications

Extracting social determinants of health from electronic health records using natural language processing: a systematic review

Extracting social determinants of health from electronic health records using natural language processing: a systematic review

CaliForest

Characterizing Opioid Overdoses Using Emergency Medical Services Data

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