{"id":"W4310462613","doi":"10.2196/37954","title":"Digital Phenotyping Data to Predict Symptom Improvement and App Personalization: Protocol for a Prospective Study","year":2022,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mental health; Personalization; mHealth; Digital health; Psychological intervention; Applied psychology; Protocol (science); Logistic regression; Psychology; Computer science; Medical education; Medicine; Data science; Internet privacy; Health care; World Wide Web; Psychiatry; Machine learning; Alternative medicine","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":[0.04395002,0.003564863,0.004395541,0.002795639,0.005220111,0.002936014,0.002123007,0.004373057,0.05300389],"category_scores_gemma":[0.04898739,0.002725539,0.004024514,0.003308411,0.002972373,0.002989588,0.002327214,0.006835324,0.01506308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005176726,"about_ca_system_score_gemma":0.02307894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004390585,"about_ca_topic_score_gemma":0.005990526,"domain_scores_codex":[0.9764426,0.01316283,0.004109759,0.001896312,0.002793004,0.001595433],"domain_scores_gemma":[0.970306,0.007614871,0.004374125,0.005305927,0.01063126,0.001767731],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"randomized_trial","study_design_gemma":"not_applicable","study_design_scores_codex":[0.3147112,0.08556114,0.02658193,0.05869968,0.003457692,0.002409999,0.006298441,0.012661,0.007433171,0.02137133,0.1737989,0.2870155],"study_design_scores_gemma":[0.3271279,0.1173376,0.07890747,0.0290792,0.002155909,0.0008537619,0.004156959,0.01276086,0.0083824,0.02168142,0.3964273,0.001129202],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.001427965,0.00009474923,0.00214193,0.0001235323,0.0001295045,0.9937679,0.001591031,0.00004758011,0.0006759185],"genre_scores_gemma":[0.0007150001,0.00003060365,0.001159237,0.0000573436,0.00000875221,0.9977878,0.000123451,0.000002815118,0.0001149022],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.05300389,"threshold_uncertainty_score":0.2324327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3875506471460707,"score_gpt":0.6203034903822804,"score_spread":0.2327528432362097,"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."}}