{"id":"W3013366438","doi":"10.2196/16008","title":"Prediction of Medical Concepts in Electronic Health Records: Similar Patient Analysis","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Health records; Medical record; Electronic health record; Computer science; Data science; Medicine; Medical emergency; Health care; Radiology","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":[0.003628245,0.0005743406,0.000506649,0.005596309,0.0005757622,0.001127304,0.0008132527,0.001090562,0.0008716185],"category_scores_gemma":[0.02010113,0.0001521022,0.001044283,0.00291938,0.0005098401,0.001810781,0.001185324,0.0008580294,0.0004159995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007406397,"about_ca_system_score_gemma":0.0009370346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003004012,"about_ca_topic_score_gemma":0.003164193,"domain_scores_codex":[0.9974906,0.0006664504,0.0004206554,0.0006925851,0.000620715,0.000108954],"domain_scores_gemma":[0.9866914,0.009001738,0.001607427,0.001084969,0.001329236,0.0002852734],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009572969,0.0008304958,0.6664665,0.0004410234,0.0004929938,0.001304064,0.001459438,0.03576609,0.005949916,0.003462721,0.003377354,0.2794921],"study_design_scores_gemma":[0.0001150863,0.0007894466,0.1914124,0.0002009037,0.0004777941,0.004078016,0.001871458,0.7583978,0.01315695,0.01950559,0.009873784,0.000120735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8765272,0.001150305,0.1148477,0.0005522746,0.00008156451,0.0004977375,0.004121585,0.000504054,0.00171755],"genre_scores_gemma":[0.8737537,0.0003104346,0.1189316,0.0001255626,0.00006699927,0.000153624,0.006302669,0.00002341199,0.0003319415],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005596309,"threshold_uncertainty_score":0.01918817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02367315814278365,"score_gpt":0.3382679664084237,"score_spread":0.31459480826564,"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."}}