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Predicting the Risk of Suicide by Analyzing the Text of Clinical Notes
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Identification of suicidal behavior among psychiatrically hospitalized adolescents using natural language processing and machine learning of electronic health records
inferred← citeshttps://doi.org/10.1371/journal.pone.0220623openalex-snapshot
Can acute suicidality be predicted by Instagram data? Results from qualitative and quantitative language analyses
inferred← citeshttps://doi.org/10.1371/journal.pone.0250448openalex-snapshot
Characterization of time-variant and time-invariant assessment of suicidality on Reddit using C-SSRS
inferred← citeshttps://doi.org/10.1371/journal.pone.0331459openalex-snapshot
Automated detection and prediction of suicidal behavior from clinical notes using deep learning
inferred← citeshttps://doi.org/10.14569/ijacsa.2022.0130247openalex-snapshot
Processing of Clinical Notes for Efficient Diagnosis with Dual LSTM
inferred← citeshttps://doi.org/10.15265/iy-2016-017openalex-snapshot
Aspiring to Unintended Consequences of Natural Language Processing: A Review of Recent Developments in Clinical and Consumer-Generated Text Processing
inferred← citeshttps://doi.org/10.18103/mra.v11i10.4658openalex-snapshot
Essential Regulatory Update for Implementation of Clinical Decision Support Software in the Psychiatric Field
inferred← citeshttps://doi.org/10.18653/v1/w17-3101openalex-snapshot
A Cross-modal Review of Indicators for Depression Detection Systems
inferred← citeshttps://doi.org/10.20965/jaciii.2025.p0649openalex-snapshot
A Text-Based Suicide Detection Model Using Hybrid Prompt Tuning in Few-Shot Scenarios
inferred← citeshttps://doi.org/10.21814/lm.11.2.276openalex-snapshot
Extracción y análisis de las causas de suicidio a través de marcadores lingüísticos en reportes periodísticos
inferred← citeshttps://doi.org/10.2196/13216openalex-snapshot
Your Robot Therapist Will See You Now: Ethical Implications of Embodied Artificial Intelligence in Psychiatry, Psychology, and Psychotherapy
inferred← citeshttps://doi.org/10.2196/14500openalex-snapshot
Identifying the Medical Lethality of Suicide Attempts Using Network Analysis and Deep Learning: Nationwide Study
inferred← citeshttps://doi.org/10.2196/15901openalex-snapshot
Testing Suicide Risk Prediction Algorithms Using Phone Measurements With Patients in Acute Mental Health Settings: Feasibility Study
inferred← citeshttps://doi.org/10.2196/17784openalex-snapshot
Identifying and Predicting Intentional Self-Harm in Electronic Health Record Clinical Notes: Deep Learning Approach
inferred← citeshttps://doi.org/10.2196/18407openalex-snapshot
Exploring Suicidal Ideation Using an Innovative Mobile App-Strength Within Me: The Usability and Acceptability of Setting up a Trial Involving Mobile Technology and Mental Health Service Users
inferred← citeshttps://doi.org/10.2196/27017openalex-snapshot
Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method
inferred← citeshttps://doi.org/10.2196/42803openalex-snapshot
Intentional Self-Harm Among US Veterans With Traumatic Brain Injury or Posttraumatic Stress Disorder: Retrospective Cohort Study From 2008 to 2017
inferred← citeshttps://doi.org/10.2196/85085openalex-snapshot
AI Models for Suicide Risk Prediction in Adult Patients Receiving Mental Health Care Using Real-World Data: Retrospective Population-Based Study
inferred← citeshttps://doi.org/10.2196/jmir.7215openalex-snapshot
Researching Mental Health Disorders in the Era of Social Media: Systematic Review
inferred← citeshttps://doi.org/10.2196/mental.4227openalex-snapshot
Identifying Chinese Microblog Users With High Suicide Probability Using Internet-Based Profile and Linguistic Features: Classification Model
inferred← citeshttps://doi.org/10.2196/mental.4822openalex-snapshot
Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality
inferred← citeshttps://doi.org/10.2196/mental.5475openalex-snapshot
Predicting Risk of Suicide Attempt Using History of Physical Illnesses From Electronic Medical Records
inferred← citeshttps://doi.org/10.2196/preprints.85085openalex-snapshot
AI-based algorithms trained with real-world data for suicide risk prevention in adult mental health patients: The IDICIUS Project. (Preprint)
inferred← citeshttps://doi.org/10.30525/2500-946x/2024-1-1openalex-snapshot
PERSPECTIVES АND POSSIBILITIES ОF USING ARTIFICIAL INTELLIGENCE DURING AUTOGENIC TRAINING FOR PSYCHOPHYSIOLOGICAL STATE CORRECTION
inferred← citeshttps://doi.org/10.3115/v1/w14-3201openalex-snapshot
Predicting military and veteran suicide risk: Cultural aspects
inferred← citeshttps://doi.org/10.3233/shti220140openalex-snapshot
Implementing Predictive Models Within an Electronic Health Record System: Lessons from an External Validation of a Suicide Risk Model
inferred← citeshttps://doi.org/10.3389/fpsyt.2017.00192openalex-snapshot
Classification of Suicide Attempts through a Machine Learning Algorithm Based on Multiple Systemic Psychiatric Scales
inferred← citeshttps://doi.org/10.3389/fpsyt.2020.00268openalex-snapshot
Identifying Predictors of Suicide in Severe Mental Illness: A Feasibility Study of a Clinical Prediction Rule (Oxford Mental Illness and Suicide Tool or OxMIS)
inferred← citeshttps://doi.org/10.3389/fpsyt.2023.1217649openalex-snapshot
Identifying features of risk periods for suicide attempts using document frequency and language use in electronic health records
inferred← citeshttps://doi.org/10.3390/ijerph17165929openalex-snapshot
Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations
inferred← citeshttps://doi.org/10.4088/jcp.22m14568openalex-snapshot
Leveraging Natural Language Processing to Improve Electronic Health Record Suicide Risk Prediction for Veterans Health Administration Users
inferred← citeshttps://doi.org/10.4236/ojapps.2024.142037openalex-snapshot
Smart Approaches to Efficient Text Mining for Categorizing Sexual Reproductive Health Short Messages into Key Themes
inferred← citeshttps://doi.org/10.4338/aci-2016-07-ra-0119openalex-snapshot
Metrics for Electronic-Nursing-Record-Based Narratives: cross-sectional analysis
inferred← citeshttps://doi.org/10.48550/arxiv.1310.6775openalex-snapshot
Durkheim Project Data Analysis Report
inferred← citeshttps://doi.org/10.48550/arxiv.1507.01796openalex-snapshot
Classify Sina Weibo users into High or Low happiness Groups Using Linguistic and Behavior Features
inferred← citeshttps://doi.org/10.48550/arxiv.1511.04805openalex-snapshot
Job-related discourse on social media
inferred← citeshttps://doi.org/10.48550/arxiv.1701.08796openalex-snapshot
Learning from various labeling strategies for suicide-related messages on social media: An experimental study
inferred← citeshttps://doi.org/10.5114/nan.2023.129070openalex-snapshot
Application of artificial intelligence for diagnosis, prognosis and treatment in psychology: a review
inferred← citeshttps://doi.org/10.5121/csit.2020.1009openalex-snapshot
Computer Science & Information Technology
inferred← citeshttps://doi.org/10.5815/ijisa.2025.02.06openalex-snapshot
Enhancing Suicide Risk Prediction through BERT: Leveraging Textual Biomarkers for Early Detection
inferred← citeshttps://doi.org/10.64898/2026.01.12.26343914openalex-snapshot
Suicide- and crisis-risk detection using large language models in mental-health chatbots
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