{"id":"W3168583011","doi":"10.2196/24377","title":"Designing a Clinical Decision Support Tool That Leverages Machine Learning for Suicide Risk Prediction: Development Study in Partnership With Native American Care Providers","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Suicide and Self-Harm Studies","field":"Psychology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Institute of Mental Health","keywords":"Operationalization; General partnership; Clinical decision support system; Risk assessment; Poison control; Suicide prevention; Risk management; Decision support system; Machine learning; Computer science; Psychology; Risk analysis (engineering); Artificial intelligence; Medicine; Medical emergency; Computer security; Business","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04044173,0.0004992043,0.0003546358,0.001007738,0.002286031,0.002991506,0.001446591,0.0007238904,0.002680218],"category_scores_gemma":[0.07020698,0.0006634675,0.0005271057,0.0005708914,0.001197098,0.00189529,0.002929612,0.001688304,0.0005572312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002689252,"about_ca_system_score_gemma":0.01424239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005040966,"about_ca_topic_score_gemma":0.01113502,"domain_scores_codex":[0.9807716,0.0154354,0.0006785624,0.0007056754,0.001429676,0.0009791132],"domain_scores_gemma":[0.9477154,0.03091433,0.003046047,0.002434925,0.009061368,0.006827862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006636747,0.01678405,0.3164422,0.0008488193,0.0001082211,0.00285211,0.2312808,0.003047961,0.006556007,0.001325161,0.01050115,0.4095899],"study_design_scores_gemma":[0.001838088,0.02553774,0.228705,0.003173516,0.0004940128,0.005704037,0.5502239,0.07369467,0.02049401,0.003778428,0.08573052,0.0006260667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9744408,0.0001586097,0.01715292,0.001728013,0.00003364752,0.003145716,0.0001061397,0.0002164282,0.003017688],"genre_scores_gemma":[0.8505797,0.0003144248,0.1435949,0.001044455,0.00002908987,0.002608056,0.0002090623,0.00007020591,0.001550146],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04044173,"threshold_uncertainty_score":0.2138788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.110354928628173,"score_gpt":0.4108693918972556,"score_spread":0.3005144632690825,"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."}}