{"id":"W3045129222","doi":"","title":"Visual Analytics of Electronic Health Records with a focus on Acute Kidney Injury","year":2020,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Focus (optics); Visual analytics; Electronic health record; Health records; Acute kidney injury; Analytics; Medicine; Computer science; Visualization; Data science; Artificial intelligence; Political science; Internal medicine; Health care","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004368931,0.0003829548,0.0005760796,0.0005042646,0.0002343535,0.0001686422,0.00184822,0.0001597896,0.00001187196],"category_scores_gemma":[0.0000813166,0.0003900081,0.0001361948,0.001720792,0.00008944578,0.001113091,0.0005590679,0.001330355,0.0000756047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004265672,"about_ca_system_score_gemma":0.001284016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003336595,"about_ca_topic_score_gemma":0.0003485333,"domain_scores_codex":[0.9965172,0.0006147873,0.0003992286,0.0009220663,0.0007103234,0.0008364079],"domain_scores_gemma":[0.9974804,0.00009266827,0.0005193031,0.0007922717,0.0002123167,0.0009030353],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000446285,0.0001177532,0.9913259,0.0001648233,0.0001567574,0.0001842692,0.0009490749,0.0001045245,0.00007628057,0.002424054,0.000008546145,0.004041751],"study_design_scores_gemma":[0.003804461,0.02111898,0.9595887,0.0009299546,0.0001747139,0.0001031989,0.0001682645,0.0005983458,0.003745063,0.000542577,0.007629333,0.001596402],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9444011,0.00003041048,0.04093078,0.01355273,0.0001268,0.0004089699,0.00004353879,0.0003553802,0.0001502515],"genre_scores_gemma":[0.9940384,0.00005835768,0.0006617753,0.004666377,0.0000796828,0.000001024815,0.00001687087,0.00004389856,0.0004336266],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04963724,"threshold_uncertainty_score":0.9998552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05975822550037502,"score_gpt":0.343949177727872,"score_spread":0.284190952227497,"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."}}