{"id":"W4200070831","doi":"10.2196/preprints.35250","title":"Predicting Abnormal Laboratory Blood Test Results in the Intensive Care Unit Using Novel Features Based on Information Theory and Historical Conditional Probability: Observational Study (Preprint)","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial intelligence; Machine learning; Statistics; Logistic regression; Blood test; Computer science; Conditional probability; Intensive care unit; Medicine; Mathematics; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003383362,0.0003484744,0.0003496153,0.0002980651,0.0003365943,0.0004984592,0.0009159084,0.0002812655,0.000008613378],"category_scores_gemma":[0.01192258,0.0002839783,0.00006709099,0.0005545123,0.00007042526,0.0006127626,0.001158123,0.002183927,8.536855e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006485608,"about_ca_system_score_gemma":0.00164074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001157672,"about_ca_topic_score_gemma":0.0004095311,"domain_scores_codex":[0.9956489,0.001350328,0.000870191,0.0008076247,0.001036957,0.0002860124],"domain_scores_gemma":[0.9915272,0.003899244,0.0005179466,0.0011022,0.002864416,0.00008898821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001253612,0.000466325,0.7617173,0.0004964871,0.00003609517,0.00002990749,0.04287153,0.1829698,0.000009655531,0.01069937,0.00003884524,0.0005393585],"study_design_scores_gemma":[0.001236314,0.000300691,0.831359,0.0003109757,0.00002878319,0.00002415737,0.01226029,0.1533572,0.00001162183,0.00074799,0.00006286416,0.0003001034],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9606178,0.0001018765,0.03166701,0.003603749,0.0005487872,0.002478234,0.000334173,0.0002091644,0.0004391861],"genre_scores_gemma":[0.9778727,0.000001163087,0.01839584,0.002875172,0.0001110853,0.0001662731,0.0005579363,0.00001169008,0.000008170248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06964177,"threshold_uncertainty_score":0.9999613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07610481882840009,"score_gpt":0.3150481395990111,"score_spread":0.238943320770611,"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."}}