{"id":"W3163682701","doi":"10.2196/25304","title":"Combining External Medical Knowledge for Improving Obstetric Intelligent Diagnosis: Model Development and Validation","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Social Science Fund of China; China Postdoctoral Science Foundation","keywords":"Interpretability; Computer science; Medical knowledge; Artificial intelligence; Medical diagnosis; Benchmark (surveying); Process (computing); Medical record; Machine learning; Data science; Data mining; 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":[0.003078112,0.001150916,0.0008147761,0.001385775,0.0004635051,0.001058067,0.001815615,0.001441388,0.001466585],"category_scores_gemma":[0.006487015,0.0004169257,0.001134276,0.0008520311,0.0005706167,0.001476965,0.001266099,0.001989815,0.0003388714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002405112,"about_ca_system_score_gemma":0.002797095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03848175,"about_ca_topic_score_gemma":0.02247551,"domain_scores_codex":[0.9994135,0.0001714616,0.0000431277,0.0001969277,0.00009204937,0.00008296417],"domain_scores_gemma":[0.997145,0.001659543,0.0001965399,0.0001359398,0.000768818,0.00009417972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002509668,0.0002744307,0.01339959,0.0001355515,0.0001937103,0.0001265276,0.00009432586,0.8578793,0.00123927,0.001825915,0.002193763,0.1223867],"study_design_scores_gemma":[0.000005631392,0.00001619339,0.0003707107,0.000008243522,0.00002183107,0.000008245292,0.000004962979,0.9987178,0.0002269247,0.0005266313,0.00008909146,0.000003797577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3642908,0.005362183,0.6161132,0.003841063,0.0002927122,0.0004810591,0.001224524,0.002200629,0.006193837],"genre_scores_gemma":[0.9316324,0.0006746211,0.06478053,0.0003153824,0.00007552809,0.0002623331,0.000817376,0.00002678754,0.001415062],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03848175,"threshold_uncertainty_score":0.0765155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04218233896602738,"score_gpt":0.3439405876318005,"score_spread":0.3017582486657731,"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."}}