{"id":"W2982546097","doi":"10.3390/electronics8111235","title":"A Review of Automatic Phenotyping Approaches using Electronic Health Records","year":2019,"lang":"en","type":"review","venue":"Electronics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Charles Darwin University; Trent University; Nottingham Trent University","keywords":"Computer science; Popularity; Biomedical text mining; Information retrieval; Artificial intelligence; Machine learning; Health records; Subject (documents); Data science; Information extraction; Electronic health record; Natural language processing; Health care; World Wide Web; Text mining","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.003073451,0.001046698,0.001372916,0.008364784,0.0004043741,0.001303997,0.00151044,0.001219363,0.003672663],"category_scores_gemma":[0.007864911,0.00048492,0.001493085,0.007693798,0.0005820182,0.002276566,0.0008385062,0.0009687883,0.001765843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009072786,"about_ca_system_score_gemma":0.0029878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002692892,"about_ca_topic_score_gemma":0.003283827,"domain_scores_codex":[0.9984879,0.000386988,0.0003603284,0.0002548371,0.0004610367,0.00004887388],"domain_scores_gemma":[0.9931559,0.004933572,0.0005273853,0.0001524464,0.001148988,0.00008157269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.00004917913,0.00004309555,0.0004676972,0.06675053,0.0002096378,0.0001286932,0.0001434635,0.0003167102,0.0007129551,0.001861395,0.01273037,0.9165862],"study_design_scores_gemma":[0.00002494752,0.0001538886,0.005299888,0.07117847,0.00129874,0.001940093,0.0003168016,0.000453539,0.001673579,0.003102875,0.9144715,0.000085748],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002264907,0.996822,0.001154971,0.0003813476,0.0001329836,0.00002731445,0.000120286,0.00002753635,0.001107024],"genre_scores_gemma":[0.001052212,0.9962089,0.001983426,0.0002490171,0.00008488783,0.00002847592,0.0001573489,0.000005759555,0.0002300383],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.008364784,"threshold_uncertainty_score":0.01625419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1107684030840043,"score_gpt":0.3702392415391,"score_spread":0.2594708384550957,"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."}}