{"id":"W3157931152","doi":"10.2196/30153","title":"Correction: Extracting Family History Information From Electronic Health Records: Natural Language Processing Analysis","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Health records; Natural language processing; Electronic health record; Natural history; Natural language; Information retrieval; Data science; Artificial intelligence; Medicine; Health care","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":[0.008243567,0.00300712,0.002833385,0.005629881,0.00395207,0.005019058,0.005448688,0.008923857,0.04709887],"category_scores_gemma":[0.155113,0.001548229,0.002526972,0.003590123,0.003848406,0.002772266,0.002939187,0.01328557,0.01928812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004588727,"about_ca_system_score_gemma":0.009526309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02806935,"about_ca_topic_score_gemma":0.03153553,"domain_scores_codex":[0.987854,0.002321833,0.002613604,0.00150592,0.004801103,0.0009035258],"domain_scores_gemma":[0.8977066,0.03282114,0.004625582,0.005173499,0.05685345,0.002819747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005659138,0.000008816868,0.0001599695,0.0003149116,0.000042273,0.0007627512,0.0001169022,0.00007097526,0.0001035106,0.000773102,0.9916719,0.005918282],"study_design_scores_gemma":[0.0001206803,0.00004919802,0.001932016,0.001437233,0.0001631064,0.002974317,0.0003459263,0.0008561544,0.0007533581,0.003096402,0.9881363,0.0001352367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"editorial","genre_gemma":"methods","genre_scores_codex":[0.000389768,0.0009605372,0.002046306,0.07840961,0.913385,0.0000433139,0.002556621,0.0007605017,0.001448278],"genre_scores_gemma":[0.04239631,0.01248366,0.01857842,0.200956,0.5859722,0.0006658846,0.007597781,0.004032085,0.1273177],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.04709887,"threshold_uncertainty_score":0.1575614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01152834897830278,"score_gpt":0.3091506947809656,"score_spread":0.2976223458026628,"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."}}