{"id":"W3023309646","doi":"10.2196/19055","title":"Retracted: Medical Data Feature Learning Based on Probability and Depth Learning Mining: Model Development and Validation","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":6,"is_retracted":true,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; Big data; Health care; Deep learning; Feature (linguistics); Feature selection; Classifier (UML); Data mining; Data science","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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.007788919,0.001121372,0.001052301,0.001134182,0.0006875025,0.001290424,0.004280942,0.00240309,0.007288033],"category_scores_gemma":[0.03769482,0.0005956242,0.001577751,0.001527505,0.00088335,0.003597185,0.002292173,0.004064662,0.002865863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001781369,"about_ca_system_score_gemma":0.003061965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01789648,"about_ca_topic_score_gemma":0.00902566,"domain_scores_codex":[0.9978786,0.0005996768,0.0001721885,0.0003703324,0.0008244345,0.000154813],"domain_scores_gemma":[0.9786361,0.007738507,0.0005931212,0.002556133,0.009985866,0.0004903202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003350726,0.0003947605,0.01180364,0.0004904092,0.0002491985,0.0002807982,0.0003603994,0.3654377,0.002897027,0.02465538,0.07510173,0.5179939],"study_design_scores_gemma":[0.00002225722,0.00008127791,0.001135639,0.00005386357,0.00002543631,0.00005744223,0.00003012295,0.9811342,0.002047946,0.007520392,0.00787018,0.00002117115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03271678,0.001649388,0.9437443,0.009452096,0.002590104,0.0004196825,0.001119535,0.003338419,0.004969683],"genre_scores_gemma":[0.4465823,0.001900109,0.5250255,0.002103165,0.001245408,0.0008111533,0.003885491,0.0008760949,0.01757079],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9975969,"threshold_uncertainty_score":0.04119229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06835565563021183,"score_gpt":0.3328681674522556,"score_spread":0.2645125118220437,"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."}}