{"id":"W2884514357","doi":"10.2196/mental.9766","title":"An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support","year":2018,"lang":"en","type":"article","venue":"JMIR Mental Health","topic":"Suicide and Self-Harm Studies","field":"Psychology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Instituto de Salud Carlos III; American Foundation for Suicide Prevention","keywords":"Clinical decision support system; Context (archaeology); Data mining; Decision tree; Suicide Risk; Computer science; Decision support system; Cluster analysis; Data science; Medicine; Poison control; Suicide prevention; Medical emergency; Machine learning","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":[0.02037357,0.001646998,0.002609505,0.01397748,0.001347333,0.006285311,0.002890631,0.001455068,0.0016062],"category_scores_gemma":[0.05482912,0.00116487,0.003596988,0.01083981,0.00078696,0.002726177,0.002450594,0.002289185,0.001052633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001817338,"about_ca_system_score_gemma":0.004233758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00570031,"about_ca_topic_score_gemma":0.006910481,"domain_scores_codex":[0.9812924,0.009210606,0.003767866,0.002107923,0.003374002,0.000247041],"domain_scores_gemma":[0.9608666,0.02719673,0.003038077,0.003569776,0.004624981,0.0007037996],"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":[0.0006904324,0.001234061,0.04174843,0.003818736,0.002494659,0.001030995,0.002633481,0.02700186,0.009482887,0.01393628,0.01269821,0.88323],"study_design_scores_gemma":[0.0004172096,0.0009066316,0.02900322,0.002575456,0.00150603,0.002380223,0.004536118,0.8184418,0.01658581,0.08331189,0.03989432,0.0004411855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01556782,0.001200555,0.9700115,0.002201678,0.0001099574,0.002311893,0.003565308,0.004134827,0.0008964198],"genre_scores_gemma":[0.03976918,0.0003083071,0.9570514,0.0001488425,0.00003548936,0.001024294,0.001466978,0.00004986518,0.0001457254],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02037357,"threshold_uncertainty_score":0.107747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2135389183274607,"score_gpt":0.5266761393117934,"score_spread":0.3131372209843326,"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."}}