{"id":"W4360613735","doi":"10.1038/s41746-023-00772-4","title":"Complex modeling with detailed temporal predictors does not improve health records-based suicide risk prediction","year":2023,"lang":"en","type":"article","venue":"npj Digital Medicine","topic":"Suicide and Self-Harm Studies","field":"Psychology","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Food and Drug Administration; Hamilton Health Sciences Foundation; National Institute of Mental Health; U.S. Department of Health and Human Services","keywords":"Random forest; Logistic regression; Predictive modelling; Machine learning; Ensemble learning; Computer science; Artificial intelligence; Receiver operating characteristic; Artificial neural network; Ensemble forecasting; Regression; Mental health; Statistics; Psychology; Mathematics; Psychiatry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000616465,0.0003879301,0.0006933119,0.0003515311,0.0002996986,0.00004295321,0.000218968,0.000119106,0.000260865],"category_scores_gemma":[0.000282301,0.000237818,0.00009633036,0.0006047403,0.0002556333,0.0002335122,0.00005818175,0.000384531,0.0001588416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001178709,"about_ca_system_score_gemma":0.000110552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001918081,"about_ca_topic_score_gemma":0.0007531911,"domain_scores_codex":[0.9970415,0.0001104549,0.0007935129,0.0006990283,0.0006077141,0.000747768],"domain_scores_gemma":[0.9983,0.00038968,0.0003289137,0.000513659,0.0001666367,0.0003010969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001982648,0.0002346716,0.9554489,0.0001395944,0.0006550477,0.0001106322,0.00457025,0.0008708829,0.0002356494,0.000162101,0.01461864,0.02097103],"study_design_scores_gemma":[0.02638447,0.01732552,0.7295191,0.0005979855,0.0006524327,0.00005658276,0.03462247,0.1797116,0.0002174734,0.002096153,0.007018816,0.001797447],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9767204,0.0002219372,0.006419543,0.004870865,0.001851052,0.0009233521,0.0008542692,0.001179873,0.006958733],"genre_scores_gemma":[0.9957044,0.0000612701,0.0001243472,0.001040228,0.0008237592,0.0001480248,0.0006694261,0.00007465674,0.001353854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2259298,"threshold_uncertainty_score":0.9697937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05809156678828752,"score_gpt":0.325537554484045,"score_spread":0.2674459876957574,"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."}}