{"id":"W4283445681","doi":"10.2196/35807","title":"Predicting Depression in Adolescents Using Mobile and Wearable Sensors: Multimodal Machine Learning–Based Exploratory Study","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Institute of Mental Health; National Institutes of Health; University of Pittsburgh","keywords":"Depression (economics); Wearable computer; Population; Psychological intervention; Medicine; Data collection; Psychology; Artificial intelligence; Computer science; Psychiatry; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000977342,0.0004628799,0.0004087207,0.0006135151,0.0002407739,0.0004193858,0.0003589934,0.0004177275,0.0006007499],"category_scores_gemma":[0.001741833,0.0001978198,0.0007586675,0.0004204445,0.0002258526,0.0003564575,0.0004268908,0.00038626,0.0001755001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002613104,"about_ca_system_score_gemma":0.000220402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002959784,"about_ca_topic_score_gemma":0.003319929,"domain_scores_codex":[0.9996588,0.000154916,0.00002162305,0.00007419146,0.00004312298,0.00004740345],"domain_scores_gemma":[0.9993761,0.0003330589,0.00008570628,0.00005513533,0.00009813509,0.00005191839],"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":[0.0009803593,0.004101857,0.9305197,0.0001450123,0.0003020201,0.0007629964,0.001989923,0.006125119,0.006566927,0.0002270001,0.0006796013,0.04759948],"study_design_scores_gemma":[0.00009682315,0.00430342,0.8530505,0.00006674833,0.0003050534,0.0007538219,0.004728447,0.1318625,0.003147975,0.0004730267,0.001160443,0.00005127416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986094,0.00003145738,0.001078266,0.00002046122,0.000001927814,0.00005056223,0.0001115658,0.00000571386,0.00009064372],"genre_scores_gemma":[0.9968928,0.00005858407,0.002553255,0.00001738894,0.000006462745,0.00008049764,0.0002799878,0.000002343903,0.0001086817],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002959784,"threshold_uncertainty_score":0.005885124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09891382575492128,"score_gpt":0.4764249230331566,"score_spread":0.3775110972782354,"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."}}