{"id":"W4407246962","doi":"10.1371/journal.pdig.0000734","title":"Forecasting mental states in schizophrenia using digital phenotyping data","year":2025,"lang":"en","type":"article","venue":"PLOS Digital Health","topic":"Mental Health Research Topics","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Douglas Mental Health University Institute","funders":"Fonds de Recherche du Québec - Santé; Courtois Foundation","keywords":"Artificial intelligence; Machine learning; Computer science; Ordinal data; Binary classification; Ordinal regression; Regression; Skewness; Gradient boosting; Binary number; Schizophrenia (object-oriented programming); Statistics; Support vector machine; Random forest; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.000732368,0.0006620627,0.0003111244,0.0007916662,0.0001577213,0.000485385,0.0003335658,0.000405643,0.0008728544],"category_scores_gemma":[0.002338824,0.0001448899,0.0005089612,0.0004662665,0.0001610237,0.0003624854,0.0005324914,0.0005526475,0.0003761696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006211166,"about_ca_system_score_gemma":0.0004816311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0194523,"about_ca_topic_score_gemma":0.02162365,"domain_scores_codex":[0.9998258,0.00005096324,0.00001721842,0.00004621178,0.00002800653,0.00003194077],"domain_scores_gemma":[0.9994822,0.0002373556,0.00009680773,0.0000551517,0.00007946565,0.0000489874],"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":[0.00115412,0.0003827947,0.5547258,0.000252973,0.0002358072,0.0005241582,0.0002801311,0.2850116,0.005014823,0.0009668358,0.007476876,0.1439741],"study_design_scores_gemma":[0.00003579786,0.000256659,0.2081678,0.0001086234,0.00009125382,0.0001791236,0.0003372612,0.783193,0.003017093,0.002173403,0.00239027,0.00004974632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9817333,0.0005827273,0.00921423,0.0006591771,0.00007856973,0.00005307201,0.006339785,0.0003727011,0.0009664962],"genre_scores_gemma":[0.9873635,0.0002783915,0.005470138,0.00007281145,0.00002743433,0.00003384288,0.006292331,0.000008770555,0.0004526754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0194523,"threshold_uncertainty_score":0.03867817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2561580101767793,"score_gpt":0.456723695974789,"score_spread":0.2005656857980097,"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."}}