{"id":"W4307035084","doi":"10.2196/38557","title":"Lifting Hospital Electronic Health Record Data Treasures: Challenges and Opportunities","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Workflow; Raw data; Computer science; Data science; Pipeline (software); Data warehouse; Data collection; Big data; Health records; Information retrieval; Data mining; Health care; Database","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002559352,0.0001488269,0.0002592436,0.0001249581,0.0004452615,0.00007842408,0.00194749,0.00006208966,0.00007101316],"category_scores_gemma":[0.0002703197,0.0001394002,0.00002369051,0.000140281,0.00006873854,0.0006351118,0.002642067,0.001095297,0.000006557922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000129479,"about_ca_system_score_gemma":0.001212931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006663263,"about_ca_topic_score_gemma":0.00003886671,"domain_scores_codex":[0.9971408,0.0002941753,0.0006307653,0.0002214548,0.001141121,0.0005717323],"domain_scores_gemma":[0.9980075,0.0002425649,0.0003243226,0.0009593344,0.0000377193,0.0004285876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002407989,0.00003913231,0.0001911351,0.0002864508,0.00001198968,0.00001042899,0.02874621,0.000003439579,8.197112e-9,0.0262301,0.004462795,0.9400159],"study_design_scores_gemma":[0.0004439952,0.001238667,0.0009694667,0.0001067594,0.000002937319,0.0001923581,0.01331457,0.4017785,1.119219e-7,0.001056135,0.5805976,0.0002988021],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1546199,0.0443586,0.06563384,0.7049497,0.003819839,0.003391187,0.0001599792,0.003274809,0.01979208],"genre_scores_gemma":[0.9243655,0.02452565,0.0238186,0.02574379,0.0005311986,0.0003303584,0.0002812879,0.00005746075,0.0003462019],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9397171,"threshold_uncertainty_score":0.5684577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08767539339494798,"score_gpt":0.3431049919837595,"score_spread":0.2554295985888115,"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."}}