{"id":"W2053479646","doi":"10.4137/bii.s4706","title":"Suicide Note Classification Using Natural Language Processing: A Content Analysis","year":2010,"lang":"en","type":"article","venue":"Biomedical Informatics Insights","topic":"Mental Health via Writing","field":"Psychology","cited_by":239,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor; Windsor Clinical Research","funders":"U.S. National Library of Medicine","keywords":"Categorization; Mental health; Suicide prevention; Psychology; Occupational safety and health; Human factors and ergonomics; Injury prevention; Poison control; Suicide attempt; Artificial intelligence; Psychiatry; Machine learning; Medicine; Medical emergency; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003013259,0.0003370111,0.0003556376,0.005296305,0.0005034102,0.001341285,0.0004358312,0.0004378331,0.001308664],"category_scores_gemma":[0.01666204,0.0001200664,0.0005039827,0.002482611,0.0005955021,0.001314583,0.0008556593,0.0004160756,0.0004285852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009984619,"about_ca_system_score_gemma":0.0008317505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002499124,"about_ca_topic_score_gemma":0.002301503,"domain_scores_codex":[0.9972205,0.00151552,0.0003754884,0.0002833846,0.0005006844,0.000104316],"domain_scores_gemma":[0.9763925,0.01853574,0.001115011,0.0007584412,0.003029708,0.0001684714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001490574,0.001220988,0.2126012,0.001579415,0.0002426729,0.0009352569,0.01605997,0.004271101,0.03445453,0.003089389,0.004974515,0.7190804],"study_design_scores_gemma":[0.0002448742,0.001802946,0.6187309,0.0007341261,0.0006229633,0.00266657,0.02984262,0.252802,0.05767485,0.01196434,0.02256361,0.0003502129],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9175465,0.0003898874,0.07375561,0.0004910936,0.00005301616,0.00154744,0.002295669,0.0004695933,0.003451162],"genre_scores_gemma":[0.8420543,0.0002914726,0.1519458,0.000120557,0.00006849241,0.00105397,0.003419182,0.00004292949,0.001003374],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005296305,"threshold_uncertainty_score":0.01593578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1168740545948059,"score_gpt":0.42183088917571,"score_spread":0.3049568345809041,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}