{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008708449,0.000178028,0.0003602514,0.0002850784,0.0002128217,0.0001886867,0.0005994815,0.0001659679,0.000174774],"category_scores_gemma":[0.0006665792,0.0001699453,0.0001233313,0.001218173,0.00003761057,0.002281195,0.000212026,0.00146912,0.00006458175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001049105,"about_ca_system_score_gemma":0.002918422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007234818,"about_ca_topic_score_gemma":0.0003492934,"domain_scores_codex":[0.9969427,0.0001948756,0.001039224,0.0001666457,0.001134492,0.0005220377],"domain_scores_gemma":[0.9979819,0.0002741988,0.0007019475,0.000495572,0.0002427755,0.0003035798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004585399,0.00002767255,0.000923446,0.0001851081,0.00006194699,0.000009606403,0.04154048,0.0001271129,0.000001603616,0.0002210313,0.01196268,0.9449347],"study_design_scores_gemma":[0.0002165877,0.00004144618,0.004556968,0.0001352543,0.00001748707,0.00003941351,0.00507215,0.9165023,0.000006359332,0.00002521351,0.07320491,0.0001819361],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06035129,0.008891258,0.913148,0.004760381,0.004516902,0.0003812207,0.000006228929,0.00104207,0.006902627],"genre_scores_gemma":[0.9240882,0.0002102357,0.05282034,0.02126485,0.0004112935,0.00005594162,0.0005454969,0.0000165386,0.0005870734],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9447528,"threshold_uncertainty_score":0.6930166,"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."}}