{"id":"W4313439128","doi":"10.1038/s41597-022-01899-x","title":"MIMIC-IV, a freely accessible electronic health record dataset","year":2023,"lang":"en","type":"article","venue":"Scientific Data","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":2791,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"National Institutes of Health; National Institute of Biomedical Imaging and Bioengineering; U.S. Department of Health and Human Services","keywords":"Electronic health record; Computer science; Health records; Information retrieval; World Wide Web; Health care; Political science","routes":{"ca_aff":true,"ca_fund":false,"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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.001916602,0.0006202996,0.0008503263,0.004178921,0.000428441,0.001679823,0.001724931,0.001367663,0.01304065],"category_scores_gemma":[0.02086012,0.0003703873,0.0007201384,0.004826851,0.0003212074,0.0008913463,0.002130141,0.000981804,0.01079773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001238718,"about_ca_system_score_gemma":0.004348359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008860976,"about_ca_topic_score_gemma":0.01320099,"domain_scores_codex":[0.9977086,0.0004637169,0.0006604253,0.0004854882,0.0005298337,0.0001519068],"domain_scores_gemma":[0.9921337,0.002268163,0.001600241,0.001882261,0.001444049,0.0006715113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001136972,0.0002114065,0.04701796,0.003066475,0.000381263,0.0005551895,0.0003880393,0.001816345,0.002579891,0.003972177,0.8810318,0.05784248],"study_design_scores_gemma":[0.0007018671,0.0002753893,0.1022807,0.0008369495,0.0002709766,0.0009272221,0.0004756284,0.00396653,0.00285186,0.004592793,0.8826838,0.0001364393],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005239309,0.0003324289,0.001734872,0.0003753951,0.00007964215,0.0002831657,0.9885818,0.0008364351,0.002536972],"genre_scores_gemma":[0.01163946,0.0002693173,0.00475143,0.0002546477,0.00007288622,0.0006529427,0.9811611,0.00008435563,0.001113949],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.998275,"threshold_uncertainty_score":0.04362535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1084604384544312,"score_gpt":0.394378858073248,"score_spread":0.2859184196188168,"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."}}