{"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":"codex-gemma-dda1882f352a","candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002252336,0.0002196327,0.000288405,0.00008206272,0.0003010035,0.0001902817,0.001007911,0.000454076,0.00002900605],"category_scores_gemma":[0.006691353,0.0001846162,0.00001850394,0.0003229679,0.0001126313,0.0006236234,0.001055066,0.002503017,0.0000104004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004904077,"about_ca_system_score_gemma":0.0008602894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003522068,"about_ca_topic_score_gemma":0.000005450115,"domain_scores_codex":[0.9963387,0.0002557719,0.0006396212,0.0003927169,0.002019896,0.0003532409],"domain_scores_gemma":[0.9976948,0.0005598472,0.0002640832,0.0004511855,0.00009061266,0.0009393978],"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":[0.00007791261,0.0001422985,0.06700553,0.002840307,0.00003653092,0.00005639291,0.05951644,0.02462669,0.000002079386,0.002233582,0.004002091,0.8394601],"study_design_scores_gemma":[0.0004707798,0.0001628967,0.002827419,0.0002308521,0.000003834641,0.00001927963,0.0002780171,0.9849313,0.000009996059,0.00003108551,0.0108393,0.0001951821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2886814,0.00004802862,0.6764554,0.0319825,0.0001085879,0.0005727338,0.000002642168,0.0006227812,0.001525931],"genre_scores_gemma":[0.7055817,0.00002061781,0.2883818,0.005662764,0.0001030487,0.00002537664,0.0001762034,0.00001725803,0.00003128247],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9603047,"threshold_uncertainty_score":0.9997982,"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."}}