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Integration and Validation of a Natural Language Processing Machine Learning Suicide Risk Prediction Model Based on Open-Ended Interview Language in the Emergency Department.
Cohen, Joshua; Wright-Berryman, Jennifer; Rohlfs, Lesley; Trocinski, Douglas; Daniel, LaMonica; Klatt, Thomas W.
  • Cohen J; Clarigent Health, Mason, OH, United States.
  • Wright-Berryman J; Department of Social Work, College of Allied Health Sciences, University of Cincinnati, Cincinnati, OH, United States.
  • Rohlfs L; Clarigent Health, Mason, OH, United States.
  • Trocinski D; WPP Emergency Services, Raleigh, NC, United States.
  • Daniel L; WPP Clinical Research, Raleigh, NC, United States.
  • Klatt TW; Behavioral Health Network, Raleigh, NC, United States.
Front Digit Health ; 4: 818705, 2022.
Article in English | MEDLINE | ID: covidwho-1834377
ABSTRACT

BACKGROUND:

Emergency departments (ED) are an important intercept point for identifying suicide risk and connecting patients to care, however, more innovative, person-centered screening tools are needed. Natural language processing (NLP) -based machine learning (ML) techniques have shown promise to assess suicide risk, although whether NLP models perform well in differing geographic regions, at different time periods, or after large-scale events such as the COVID-19 pandemic is unknown.

OBJECTIVE:

To evaluate the performance of an NLP/ML suicide risk prediction model on newly collected language from the Southeastern United States using models previously tested on language collected in the Midwestern US.

METHOD:

37 Suicidal and 33 non-suicidal patients from two EDs were interviewed to test a previously developed suicide risk prediction NLP/ML model. Model performance was evaluated with the area under the receiver operating characteristic curve (AUC) and Brier scores.

RESULTS:

NLP/ML models performed with an AUC of 0.81 (95% CI 0.71-0.91) and Brier score of 0.23.

CONCLUSION:

The language-based suicide risk model performed with good discrimination when identifying the language of suicidal patients from a different part of the US and at a later time period than when the model was originally developed and trained.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study Language: English Journal: Front Digit Health Year: 2022 Document Type: Article Affiliation country: Fdgth.2022.818705

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study Language: English Journal: Front Digit Health Year: 2022 Document Type: Article Affiliation country: Fdgth.2022.818705