{"id":"W4394890223","doi":"10.3389/fpsyt.2024.1406826","title":"Editorial: Machine learning and big data analytics in mood disorders","year":2024,"lang":"en","type":"editorial","venue":"Frontiers in Psychiatry","topic":"Mental Health Research Topics","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Special Project for Research and Development in Key areas of Guangdong Province; National Natural Science Foundation of China","keywords":"Mood; Mood disorders; Big data; Cognition; Depression (economics); Psychology; Stroke (engine); Psychiatry; Clinical psychology; Data science; Medicine; Computer science; Data mining; Anxiety","routes":{"ca_aff":true,"ca_fund":false,"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":[0.01513646,0.005504169,0.007908365,0.006614779,0.005377111,0.01269763,0.00510668,0.02370545,0.02073167],"category_scores_gemma":[0.05538122,0.001911966,0.00528756,0.002887064,0.004084263,0.006216394,0.002687705,0.02769618,0.01360857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00463249,"about_ca_system_score_gemma":0.005701753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003026558,"about_ca_topic_score_gemma":0.009105549,"domain_scores_codex":[0.9903803,0.002056957,0.001458898,0.001197641,0.004311106,0.0005951701],"domain_scores_gemma":[0.9430782,0.0287865,0.003788303,0.001424272,0.01659349,0.006329148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003971922,0.00001296994,0.00002625145,0.0002040473,0.00002937195,0.00006513885,0.000008159953,0.00002017217,0.00002609541,0.0001704921,0.9974629,0.001934599],"study_design_scores_gemma":[0.0004248848,0.0000648846,0.0008755472,0.001654482,0.0002714114,0.0004417384,0.00008593912,0.000675542,0.0001419001,0.003527511,0.9917718,0.00006424094],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00002362543,0.004054015,0.0001175169,0.03733856,0.9576208,0.00002454111,0.00009019155,0.00005449036,0.0006762499],"genre_scores_gemma":[0.0001723266,0.001721379,0.00006742503,0.01556651,0.9798384,0.00002284267,0.00002803385,0.00002606579,0.002557037],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.02370545,"threshold_uncertainty_score":0.08005017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03101885392558183,"score_gpt":0.3821852288579022,"score_spread":0.3511663749323203,"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."}}