{"id":"W3007150960","doi":"10.2196/15901","title":"Testing Suicide Risk Prediction Algorithms Using Phone Measurements With Patients in Acute Mental Health Settings: Feasibility Study","year":2020,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mental health; Machine learning; Algorithm; Mood; Metric (unit); Computer science; Artificial intelligence; Poison control; Weighting; Suicide prevention; Health care; Applied psychology; Psychology; Medicine; Psychiatry; Medical emergency; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001567559,0.000377086,0.0006310352,0.0002011399,0.0006597383,0.00005567841,0.0001778458,0.00009990565,0.00002623168],"category_scores_gemma":[0.00009230111,0.000355146,0.00005151457,0.0009109774,0.0000899138,0.0003448138,0.0001043593,0.0006607275,0.00002640209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001121154,"about_ca_system_score_gemma":0.0004380793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006575391,"about_ca_topic_score_gemma":0.0009901779,"domain_scores_codex":[0.9947661,0.0008665054,0.001491358,0.001060507,0.0006599227,0.001155571],"domain_scores_gemma":[0.9973009,0.00008105101,0.0009488919,0.0003419533,0.0001360792,0.001191148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001040692,0.00358184,0.953447,0.0005968864,0.00005752217,0.000007054696,0.007869569,0.000006696767,0.000001956094,0.000004406287,0.0005459572,0.0328404],"study_design_scores_gemma":[0.007022083,0.01014812,0.9784609,0.0003330334,0.00005241893,0.0000165964,0.002956677,0.0007141609,0.000003427117,0.00003020968,0.00004066975,0.0002216841],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908311,0.0003013094,0.0001085447,0.002030039,0.0006172897,0.00496108,0.000782622,0.0001636097,0.0002043904],"genre_scores_gemma":[0.9950603,0.00001361792,0.002023596,0.002393128,0.0001119643,0.0001857884,0.0001366258,0.00005970664,0.00001524035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03261872,"threshold_uncertainty_score":0.99989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1875836950455303,"score_gpt":0.450984545950368,"score_spread":0.2634008509048377,"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."}}