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This study applies explainable, theory-informed natural language processing to analyze 17,564 crisis chat sessions from a digital helpline (2017–2021). Suicide risk was defined by explicit suicidal ideation, and logistic regression assessed psychological constructs across five conversation stages. Results show prior attempts and hopelessness as strongest predictors, with distinct gender- and age-specific pathways, supporting personalized, interpretable prevention.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/explainable-ai-for-suicide-risk-detection-gender-and-age-specific-patterns-from-real-time-crisis-chats/411244/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/explainable-ai-for-suicide-risk-detection-gender-and-age-specific-patterns-from-real-time-crisis-chats/411244.png","ImageObject",300,407,{"name":92,"@type":93},"Cipher","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-28",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How was suicide risk defined in this study?","Question",{"text":112,"@type":113},"Suicide risk was defined by explicit suicidal ideation identified in the chat sessions.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What data and time period were used for the analysis?",{"text":117,"@type":113},"The study analyzed 17,564 chat sessions from 2017–2021 from Sahar, a digital crisis helpline.",{"name":119,"@type":110,"acceptedAnswer":120},"Which factors were the strongest predictors of suicide risk?",{"text":121,"@type":113},"Previous suicide attempts and hopelessness were the strongest predictors across all examined groups.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},411244,1790790864,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144,"preview_page_count":4},687208528416,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","TYPE Original Research PUBLISHED 18 December 2025 DOI 10. 3389/fmed.2025.1703755  \nOPEN ACCESS  \nEDITED BY  \nInbar Levkovich,  \nTel-Hai College, Israel  \nREVIEWED BY  \nJennifer Wright-Berryman, University of Cincinnati, United States María Paz Raveau,  \nUniversity for Development, Chile  \n*CORRESPONDENCE  \nMeytal Grimland  \n [meytal.grimland@gmail.com](meytal.grimland@gmail.com)  \nRECEIVED 11 September 2025  \nREVISED 26 November 2025  \nACCEPTED 26 November 2025  \nPUBLISHED 18 December 2025  \nCITATION  \nGrimland M, Liberman M, Yeshayahu H, Benatov J, Munz N, Segal A, Ben Dayan L, Shenfeld I, Gal K and Levi-Belz Y (2025)  \nExplainable AI for suicide risk detection: gender-and age-specific patterns from real-time crisis chats. Front. Med. 12:1703755 .  \ndoi: 10.3389/fmed.2025.1703755  \nCOPYRIGHT  \n© 2025 Grimland, Liberman, Yeshayahu, Benatov, Munz, Segal, Ben Dayan, Shenfeld, Gal and Levi-Belz. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nExplainable AI for suicide risk detection: gender-and age-specific patterns from real-time crisis chats  \nMeytal Grimland1*, Moran Liberman2 , Hadas Yeshayahu3 , Joy Benatov3 , Noam Munz4 , Avi Segal4 , Loona Ben Dayan5 , Inbar Shenfeld5 , Kobi Gal4,6 and Yossi Levi-Belz1,7  \n1The Lior Tsfaty Center for Suicide and Mental Pain Studies, University of Haifa, Haifa, Israel,  \n2 Department of Behavioral Sciences, Ruppin Academic Center, Emek Hefer, Israel, 3 Department of Special Education, University of Haifa, Haifa, Israel, 4 Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel, 5 SAHAR, Online Mental Support, Tel Aviv, Israel, 6 School of Informatics, University of Edinburgh, Edinburgh, United Kingdom, 7 Faculty of Education, Department of Counseling and Human Development, University of Haifa, Haifa, Israel  \nBackground: Suicide remains a leading cause of death worldwide, yet conventional risk models based on static demographic or diagnostic factors show limited predictive value. Advances in explainable artificial intelligence (AI) and natural language processing (NLP) offer new opportunities for real-time, personalized risk detection.  \nMethods: We analyzed 17,564 chat sessions (2017–2021) from Sahar, a digital crisis helpline. Suicide risk (SR) was defined by explicit suicidal ideation. A theorydriven lexicon of 20 psychological constructs (e. g., hopelessness, loneliness, self-harm), derived from leading SR frameworks, was applied using NLP. Logistic regression models estimated associations between constructs and SR, stratified by gender and age (10–17, 18–20, 21–40, and 41+) . Temporal trajectories of predictors were examined across five conversation stages.  \nResults: Previous suicide attempts and hopelessness were the strongest predictors across all groups. Gender differences emerged: among women, loneliness was a consistent predictor, whereas in men, thwarted belongingness and late-session depression were more salient. Age analyses showed developmental specificity: prior attempts were strongest in adolescents, hopelessness and self-harm peaked in young adults, thwarted belongingness strengthened with age, and loneliness predicted risk only among adults aged 41+ . Several factors, including bullying/cyberbullying, LGBTQ identity, and perfectionism, were inversely associated with SR in specific subgroups. Conclusions: This study demonstrates how explainable, theory-informed NLP can capture dynamic SR factors in real-world crisis interactions. Findings reveal distinct gender-and age-specific pathways, underscoring the need for personalize","cbCaiqpIhB33tu4q","https://ap.wps.com/l/cbCaiqpIhB33tu4q","pdf",226107,"English","# Introduction\n# Methods\n# Results\n# Conclusions","[{\"question\":\"How was suicide risk defined in this study?\",\"answer\":\"Suicide risk was defined by explicit suicidal ideation identified in the chat sessions.\"},{\"question\":\"What data and time period were used for the analysis?\",\"answer\":\"The study analyzed 17,564 chat sessions from 2017–2021 from Sahar, a digital crisis helpline.\"},{\"question\":\"Which factors were the strongest predictors of suicide risk?\",\"answer\":\"Previous suicide attempts and hopelessness were the strongest predictors across all examined groups.\"}]","Explainable AI for suicide risk detection - gender-and age-specific patterns from real-time crisis chats | PDF",1790587922,23]