{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06872384,0.001586149,0.001607376,0.00792596,0.005713696,0.0215634,0.008745637,0.005887576,0.007842947],"category_scores_gemma":[0.1816491,0.001762726,0.00240517,0.01170492,0.007348855,0.03595962,0.01619732,0.01014269,0.005395783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003116109,"about_ca_system_score_gemma":0.01330446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004987473,"about_ca_topic_score_gemma":0.004425448,"domain_scores_codex":[0.9351809,0.02722162,0.006994388,0.006130188,0.0220481,0.002424897],"domain_scores_gemma":[0.7611614,0.1279495,0.01535267,0.03664609,0.04540141,0.0134888],"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":[0.0002439429,0.0005364312,0.02104685,0.003501924,0.0001533034,0.0009012849,0.01285721,0.002128829,0.0037762,0.03409559,0.1338958,0.7868626],"study_design_scores_gemma":[0.0001532219,0.0008136617,0.03219093,0.008394793,0.0002213558,0.004172008,0.06673326,0.01773891,0.008079578,0.1461949,0.7146631,0.0006443337],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"review","genre_scores_codex":[0.08579256,0.02812261,0.248871,0.5987725,0.008827476,0.001743765,0.005652586,0.004737538,0.01748011],"genre_scores_gemma":[0.2012378,0.02638626,0.6935722,0.03937877,0.01051871,0.001596624,0.01335896,0.003023119,0.01092766],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.06872384,"threshold_uncertainty_score":0.3634507,"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."}}