{"id":"W4391093163","doi":"10.1109/bigdata59044.2023.10386585","title":"Comparison of MIMIC-III and MIMIC-IV for big data analytics of health informatics","year":2023,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"University of Victoria","keywords":"Computer science; Big data; Health informatics; Usability; Informatics; Information retrieval; Data mining; Data science; Health care","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007501104,0.0006624635,0.000593418,0.002626438,0.0004714205,0.00285474,0.001342056,0.0006006277,0.001960167],"category_scores_gemma":[0.03797651,0.0003644919,0.001466513,0.001883044,0.0008181881,0.002806721,0.004035185,0.0007533589,0.0005589328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001642802,"about_ca_system_score_gemma":0.002158448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003581068,"about_ca_topic_score_gemma":0.005630143,"domain_scores_codex":[0.9930995,0.002043055,0.000821052,0.0005973084,0.00301168,0.0004275083],"domain_scores_gemma":[0.9797436,0.007253103,0.001516872,0.004174761,0.00593913,0.001372429],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.007108763,0.001993933,0.5536528,0.002476275,0.001346247,0.0005501859,0.004271512,0.05105888,0.01770522,0.02677023,0.039837,0.293229],"study_design_scores_gemma":[0.0006629126,0.006219546,0.581886,0.0005838445,0.0004867255,0.001037082,0.007512056,0.2904449,0.02263474,0.02440596,0.06380341,0.000322892],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9492455,0.0004706638,0.02155215,0.001106504,0.0001586579,0.001101646,0.004952771,0.002645287,0.01876668],"genre_scores_gemma":[0.9258552,0.000263416,0.05429975,0.0003319468,0.00005668378,0.0007802268,0.01639495,0.0002105951,0.001807212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007501104,"threshold_uncertainty_score":0.03967011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2754994223172954,"score_gpt":0.4372945017964818,"score_spread":0.1617950794791864,"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."}}