Evidence map›Paper›PMID 42343276›Full record

ArticleBMC psychiatry2026

A machine learning-based screening tool for classifying post-traumatic stress disorder in firefighters: Firefighter Latent Risk Evaluation (FLARE).

Seok Joo Chae, Johanna Inhyang Kim, Hyeontae Jo, Yun Min Song, Saebom Jeon, Sun-Young Moon, Ji-Hye Lee, Sohee Oh, Seockhoon Chung, Jae Kyoung Kim and 1 more

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Article in BMC psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Seok Joo Chae *Department of Mathematics, Soongsil University, Seoul, 06978, Republic of Korea.
Johanna Inhyang Kim *Department of Psychiatry, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea.
Hyeontae JoBiomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea.
Yun Min SongBiomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea.
Saebom JeonBiomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea.
Sun-Young MoonDepartment of Public Health Medical Services, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea.
Ji-Hye LeeDepartment of Public Health Medical Services, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea.
Sohee OhMedical Research Collaborating Center, Seoul Metropolitan Government, Boramae Medical Center, Seoul National University, Boramae-ro 5-gil, Dongjak-gu, Seoul, Republic of Korea.
Seockhoon ChungDepartment of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, 86, Asanbyeongwon-gil, Songpa-gu, Seoul, Republic of Korea.
Jae Kyoung Kim *Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea. jaekkim@kaist.ac.kr.
Jeong-Hyun Kim *Department of Public Health Medical Services, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea. jhkim@snubh.org.

Funding

Institute for Basic Science IBS-R029-C3Korea University K2418321National Research Foundation of Korea RS-2024-00357912
6 · The paper itself

Abstract

backgroundThe prevalence of post-traumatic stress disorder (PTSD) among South Korean firefighters is likely to be under-reported because of stigma and defensive attitudes. We aimed to develop a machine learning-based classification model, named Firefighter PTSD Latent Risk Evaluation (FLARE), to identify firefighters at risk of probable PTSD based on demographic and clinically relevant psychosocial features.

methodsWe analyzed data from 52,428 firefighters, including 2,305 with probable PTSD and 50,123 without PTSD, as assessed using the PTSD Checklist for DSM-5. Various clinical and occupational factors were examined, including exposure to traumatic events, depression, alcohol use disorder, insomnia, occupational stress, resilience, and awareness of and attitude towards mental illness. An extreme gradient boosting (XGBoost) algorithm was applied using the selected features to develop a FLARE model for PTSD classification. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC).

resultsThe FLARE model, comprising seven key features (decreased concentration, appetite changes, feelings of worthlessness or excessive/inappropriate guilt, sleep disturbances, perceived work pressure, poor adaptability to change, and low perceived appropriateness of seeking professional help for close others) achieved an AUROC of 0.90 in classifying probable PTSD, with a sensitivity of 0.84 and a specificity of 0.82 in the test dataset. Temporal validation using the 2024 dataset yielded an AUROC of 0.91, with a sensitivity of 0.83 and a specificity of 0.84, confirming the model's generalizability.

conclusionsThe FLARE model effectively identified firefighters at risk for PTSD using a concise set of clinical and psychosocial indicators. This tool has the potential to enhance PTSD screening and early interventions, particularly in populations with prevalent mental health stigma. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

FirefightersMachine LearningStress Disorders, Post-TraumaticAdultBoosting Machine Learning AlgorithmsClassification AlgorithmsFemaleHumansMaleMiddle AgedOccupational StressPredictive Learning ModelsRepublic of KoreaRisk AssessmentClassificationFirefightersMachine learningOccupational stressPTSDStigma

Identifiers

PMID42343276
PMCPMC13531790

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.