{"id":"W4390273464","doi":"10.18280/ria.370616","title":"Apache Spark for Analysis of Electronic Health Records: A Case Study of Diabetes Management","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Health records; SPARK (programming language); Diabetes mellitus; Diabetes management; Medicine; Electronic health record; Data science; Medical emergency; Business; Computer science; Type 2 diabetes; Political science; Health care; Endocrinology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003821492,0.00027289,0.001075492,0.001254582,0.0007387676,0.000007966679,0.0004693317,0.0001610528,0.0003069996],"category_scores_gemma":[0.0004104978,0.0002725088,0.0003313388,0.005436709,0.0001130313,0.00009175235,0.0002046144,0.0004561485,0.0001600532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003022373,"about_ca_system_score_gemma":0.0002583715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004926136,"about_ca_topic_score_gemma":0.0160703,"domain_scores_codex":[0.9943838,0.0007041669,0.002528689,0.0007122754,0.0003505537,0.001320508],"domain_scores_gemma":[0.9948652,0.002190732,0.001073152,0.001129673,0.000544661,0.0001965767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003038974,0.00283799,0.4691185,0.008346356,0.004096627,0.0001484869,0.1565887,0.1648163,0.0003779668,0.01632718,0.004370437,0.1726675],"study_design_scores_gemma":[0.0001284066,0.002004672,0.001680241,0.000426565,0.0007512612,0.00000291782,0.4720196,0.5168231,0.00156855,0.002280881,0.001957581,0.000356292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9861395,0.0003034775,0.007208214,0.0006766985,0.0005178662,0.004517496,0.00009180952,0.0001542498,0.0003907356],"genre_scores_gemma":[0.9968931,0.0002426187,0.0003136013,0.0001448825,0.00008879389,0.0009862164,0.00004349095,0.00005026791,0.001237041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4674383,"threshold_uncertainty_score":0.9999727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.22395235616812,"score_gpt":0.480949450543276,"score_spread":0.256997094375156,"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."}}