{"id":"W4393853411","doi":"10.1371/journal.pone.0301117","title":"Explainable artificial intelligence models for predicting risk of suicide using health administrative data in Quebec","year":2024,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Douglas Mental Health University Institute; Public Health Agency of Canada; Université de Montréal; Institut Universitaire en Santé Mentale de Québec; Université Laval; Institut National de Santé Publique du Québec","funders":"Government of Canada; Canadian Institute for Advanced Research","keywords":"Random forest; Population; Logistic regression; Artificial intelligence; Machine learning; Multilayer perceptron; Poison control; Suicide prevention; Suicide attempt; Medicine; Mental health; Computer science; Medical emergency; Environmental health; Psychiatry; Artificial neural network","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001660811,0.000125511,0.0002967495,0.000182471,0.0001305756,0.0001264169,0.0009138431,0.00005574595,0.000004086848],"category_scores_gemma":[0.0007338368,0.0001301496,0.00002912143,0.0005279137,0.00003909498,0.00085611,0.0003872508,0.0003730552,0.000002162656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001896123,"about_ca_system_score_gemma":0.001035093,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05176132,"about_ca_topic_score_gemma":0.02021813,"domain_scores_codex":[0.9978296,0.000241753,0.0005966942,0.0006048622,0.0003499874,0.000377136],"domain_scores_gemma":[0.9980068,0.0007945738,0.0002041416,0.0008000586,0.0001032048,0.00009126681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002100959,0.002543739,0.03072555,0.01541184,0.0004849371,0.00008432782,0.06597778,0.1970558,0.001424455,0.4251157,0.00007594025,0.2608898],"study_design_scores_gemma":[0.0000274444,0.0001865252,0.0001030938,0.001295421,0.00001304085,0.000001374379,0.0004425083,0.9778028,0.00185572,0.01816803,0.000004977145,0.00009908481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1376937,0.0009308688,0.8588225,0.001482166,0.0001006543,0.0006593916,0.0001275918,0.0001338981,0.00004921286],"genre_scores_gemma":[0.7760721,0.00003782752,0.223667,0.00003999933,0.00008590316,0.00002563474,0.00002289905,0.00001424705,0.00003441871],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7807469,"threshold_uncertainty_score":0.9976603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3637509766060374,"score_gpt":0.4095693803782702,"score_spread":0.04581840377223284,"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."}}