{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002461562,0.0006403335,0.0003554946,0.001149903,0.0006362312,0.001056364,0.001181884,0.0004884625,0.00179985],"category_scores_gemma":[0.007532633,0.0002740894,0.0006755643,0.0009691612,0.0004520107,0.0003055251,0.0004590481,0.0007688766,0.0002284485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01057146,"about_ca_system_score_gemma":0.005441411,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9561879,"about_ca_topic_score_gemma":0.9406866,"domain_scores_codex":[0.9994078,0.0002844984,0.00003462184,0.000135021,0.00005655529,0.00008147163],"domain_scores_gemma":[0.9955585,0.002790131,0.0003473329,0.0002062469,0.0009935916,0.0001041769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002435145,0.0001802756,0.5254802,0.00009844497,0.0004895381,0.0002001459,0.0004071707,0.4355472,0.00042653,0.002850999,0.006701613,0.02737442],"study_design_scores_gemma":[0.00002844935,0.00003638449,0.1018748,0.0000343865,0.00005874963,0.00001938649,0.0001286922,0.8953613,0.00009862112,0.0007733556,0.001564413,0.00002148447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9692214,0.001068707,0.01382127,0.001552014,0.00004479811,0.0001688581,0.01125514,0.0002181992,0.002649691],"genre_scores_gemma":[0.9876951,0.0002442823,0.004978283,0.0001146652,0.00001472702,0.00007967946,0.005091792,0.0000128619,0.001768664],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0438121,"threshold_uncertainty_score":0.08814019,"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."}}