{"id":"W4396610489","doi":"10.1007/s43657-023-00152-8","title":"A Multimodal Approach for Detection and Assessment of Depression Using Text, Audio and Video","year":2024,"lang":"en","type":"article","venue":"Phenomics","topic":"Mental Health via Writing","field":"Psychology","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; Mitacs","keywords":"Computer science; Artificial intelligence; Support vector machine; Mean squared error; Feature engineering; Feature selection; Machine learning; Modality (human–computer interaction); Feature (linguistics); Pattern recognition (psychology); Feature vector; Modalities; Speech recognition; Natural language processing; Deep learning; Statistics; 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":[0.0005868014,0.0006908581,0.0004070478,0.002302622,0.0002560799,0.0008428387,0.0004370206,0.0008956916,0.005852951],"category_scores_gemma":[0.001667497,0.0001852787,0.0003919248,0.001005667,0.0001322279,0.0005254115,0.0008221501,0.0003938118,0.002373157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001443973,"about_ca_system_score_gemma":0.0002463714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00129335,"about_ca_topic_score_gemma":0.002591093,"domain_scores_codex":[0.9996378,0.00008934497,0.00002869385,0.0001062258,0.00009970032,0.00003810413],"domain_scores_gemma":[0.9993052,0.0002432522,0.00007011677,0.00005435387,0.0002647588,0.00006215819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00117777,0.0003659863,0.01858979,0.0006355259,0.0001696373,0.0006119923,0.0004712937,0.0007275327,0.279847,0.0003466273,0.006591511,0.6904654],"study_design_scores_gemma":[0.0004824042,0.003604619,0.4977001,0.0007639729,0.001690187,0.0125157,0.003347396,0.1411531,0.2897326,0.006732279,0.04175586,0.0005218406],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4336672,0.004705532,0.5191422,0.001100396,0.0006744026,0.002260578,0.01423691,0.005099329,0.01911353],"genre_scores_gemma":[0.5326692,0.002460496,0.4437398,0.0006434804,0.0005717832,0.001630298,0.004101533,0.0002631074,0.0139202],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005852951,"threshold_uncertainty_score":0.01958001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06689702364809241,"score_gpt":0.4088359234359438,"score_spread":0.3419388997878514,"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."}}