{"id":"W2949497754","doi":"10.2196/11966","title":"Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations","year":2019,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":187,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Big data; Computer science; mHealth; Data science; Health care; Deep learning; Artificial intelligence; Domain (mathematical analysis); Categorization; Data mining","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":[0.005949064,0.0006666086,0.0007121867,0.0009987382,0.0006975769,0.003074339,0.001393536,0.003675876,0.007803363],"category_scores_gemma":[0.01372531,0.0002736121,0.0005928924,0.0007740462,0.002190894,0.003994057,0.002576197,0.005578483,0.001082193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001836409,"about_ca_system_score_gemma":0.00228751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002878996,"about_ca_topic_score_gemma":0.003389018,"domain_scores_codex":[0.9980845,0.001102586,0.00008729973,0.0001877873,0.0003652983,0.0001724515],"domain_scores_gemma":[0.9931449,0.004990456,0.0001752467,0.0002544087,0.001077059,0.0003580738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000230832,0.0004215899,0.004264319,0.001727465,0.0001482185,0.0005712183,0.0007686825,0.01366235,0.001409369,0.3592984,0.05489453,0.5626031],"study_design_scores_gemma":[0.0001293216,0.0004301545,0.003141698,0.003606899,0.00008574017,0.001115846,0.001394157,0.09180249,0.002878583,0.6684608,0.2268722,0.00008213414],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01240355,0.1197698,0.2172437,0.5997066,0.002151909,0.000234528,0.0003024286,0.0004038533,0.04778364],"genre_scores_gemma":[0.4754617,0.1933984,0.2081508,0.07973972,0.008091844,0.001055471,0.0004813658,0.0003385046,0.03328234],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007803363,"threshold_uncertainty_score":0.03146201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04569724367189119,"score_gpt":0.3935224048884328,"score_spread":0.3478251612165416,"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."}}