{"id":"W4223489644","doi":"10.2196/36481","title":"Big Data Health Care Platform With Multisource Heterogeneous Data Integration and Massive High-Dimensional Data Governance for Large Hospitals: Design, Development, and Application","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"West China Hospital, Sichuan University; National Health Commission of the People's Republic of China; Sichuan University","keywords":"Big data; Data governance; Data science; Computer science; Data integration; Health care; Corporate governance; Data mining; Process management; Database; Business; Operations management; Engineering; Data quality","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":[0.005976519,0.0006817323,0.0004098387,0.001218977,0.0009179155,0.002727009,0.002724217,0.0009261188,0.001786969],"category_scores_gemma":[0.003919004,0.0004375763,0.0009671228,0.001252645,0.001018859,0.003595478,0.004217866,0.001382936,0.0006040297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001327196,"about_ca_system_score_gemma":0.00426959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003142921,"about_ca_topic_score_gemma":0.001791555,"domain_scores_codex":[0.9963173,0.001070486,0.0003071883,0.0004777777,0.001386646,0.0004406734],"domain_scores_gemma":[0.9970827,0.0003032243,0.0002011977,0.0004890541,0.001143701,0.00078018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001510432,0.002696236,0.05985996,0.002115469,0.0004717337,0.003404288,0.006823778,0.04910193,0.07573271,0.1470846,0.05808251,0.5931163],"study_design_scores_gemma":[0.0008839821,0.003900682,0.03350589,0.0007840869,0.000532635,0.001724755,0.004632088,0.5809775,0.09114836,0.04651548,0.2348453,0.0005493064],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1851182,0.001475416,0.7762837,0.004014049,0.0004856506,0.00738016,0.0008294845,0.006549019,0.01786428],"genre_scores_gemma":[0.458436,0.001185652,0.5274356,0.0009469235,0.0001585012,0.003284621,0.002490452,0.0004558573,0.005606391],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005976519,"threshold_uncertainty_score":0.03160721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1724050826182084,"score_gpt":0.4389897851271564,"score_spread":0.266584702508948,"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."}}