{"id":"W4393932986","doi":"10.2196/53367","title":"Moving Biosurveillance Beyond Coded Data Using AI for Symptom Detection From Physician Notes: Retrospective Cohort Study","year":2024,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"National Institute of Child Health and Human Development; Eunice Kennedy Shriver National Institute of Child Health and Human Development; Centers for Disease Control and Prevention; U.S. Department of Health and Human Services; National Institutes of Health; National Center for Advancing Translational Sciences","keywords":"Medicine; Emergency department; Retrospective cohort study; Gold standard (test); Cohort; Medical record; Population; Cohort study; Artificial intelligence; Pediatrics; Internal medicine; Computer science; Psychiatry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01540201,0.0001548118,0.0004323954,0.0004540867,0.0001544916,0.0007154605,0.003559121,0.0001664081,0.00006050128],"category_scores_gemma":[0.006741573,0.0001225155,0.0000982011,0.0008157376,0.0001309315,0.0006941878,0.001494548,0.002769719,0.00001350456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006180746,"about_ca_system_score_gemma":0.0007280365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003473766,"about_ca_topic_score_gemma":0.0007672169,"domain_scores_codex":[0.9930751,0.001148294,0.0007211004,0.000703445,0.003868195,0.0004838046],"domain_scores_gemma":[0.9946889,0.002934947,0.00019727,0.0008668919,0.0009713641,0.0003406784],"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":[0.0004258631,0.00102924,0.5566065,0.0005581992,0.001693389,0.001957267,0.01003676,0.0004974676,0.002577305,0.002520037,0.01155462,0.4105433],"study_design_scores_gemma":[0.0004189917,0.000880865,0.03741243,0.0005951343,0.00001546858,0.00006197467,0.0001821914,0.9573224,0.0002468894,0.001576719,0.001171295,0.0001156324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4750007,0.0006604809,0.5169896,0.005154007,0.001638715,0.0004434356,0.00002159313,0.00005239648,0.00003906496],"genre_scores_gemma":[0.9947374,0.00003822968,0.003217021,0.0002174291,0.001709206,0.000009451967,0.000006629331,0.00002551008,0.00003910365],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.956825,"threshold_uncertainty_score":0.9995309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09701677003369678,"score_gpt":0.4697181986049142,"score_spread":0.3727014285712174,"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."}}