{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007952032,0.0005108171,0.0004326527,0.001824755,0.002227013,0.003123421,0.001586716,0.00153438,0.001264282],"category_scores_gemma":[0.02249443,0.0003147766,0.000858694,0.003643022,0.001215275,0.002048732,0.002125361,0.001881806,0.0007102265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00185943,"about_ca_system_score_gemma":0.003083232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008829601,"about_ca_topic_score_gemma":0.01342504,"domain_scores_codex":[0.9921502,0.003497746,0.000724999,0.0007676079,0.002271546,0.0005878358],"domain_scores_gemma":[0.983834,0.009832016,0.0009735126,0.001888853,0.002102751,0.001368906],"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":[0.001806937,0.004035648,0.2525486,0.001944628,0.0005110385,0.06934793,0.03383048,0.01953817,0.01554788,0.02687995,0.08107828,0.4929305],"study_design_scores_gemma":[0.0007408186,0.001875046,0.1815019,0.001519505,0.0005282247,0.05485792,0.07128856,0.1789699,0.05931436,0.0496968,0.3990828,0.0006241704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8794983,0.002250685,0.07092494,0.02338191,0.0003252128,0.0008190513,0.002694111,0.00194787,0.01815802],"genre_scores_gemma":[0.8822951,0.001749505,0.1073249,0.001889166,0.0002125272,0.000167169,0.001797377,0.0005102303,0.004053938],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.008829601,"threshold_uncertainty_score":0.04205489,"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."}}