{"id":"W4387007762","doi":"10.1101/2023.09.24.23295960","title":"Moving Biosurveillance Beyond Coded Data: AI for Symptom Detection from Physician Notes","year":2023,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"National Institute of Child Health and Human Development; Centers for Disease Control and Prevention; U.S. Department of Health and Human Services","keywords":"Recall; Emergency department; Medicine; Gold standard (test); Artificial intelligence; Electronic health record; Health records; Population; Retrospective cohort study; Coronavirus disease 2019 (COVID-19); Cohort; Natural language processing; Pediatrics; Computer science; Health care; Psychology; Disease; Psychiatry; Internal medicine; Infectious disease (medical specialty)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006108072,0.0009714141,0.0005882899,0.004417229,0.0005459311,0.002669452,0.001395425,0.0009694235,0.001598782],"category_scores_gemma":[0.03264129,0.0003488159,0.0009593305,0.001886475,0.0006814727,0.002126756,0.001598271,0.001447236,0.0008643055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009022682,"about_ca_system_score_gemma":0.001357313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009477912,"about_ca_topic_score_gemma":0.00576026,"domain_scores_codex":[0.9957327,0.002191989,0.0004341042,0.001006317,0.0005085866,0.0001262357],"domain_scores_gemma":[0.9675578,0.0258869,0.001796068,0.001766807,0.002610649,0.000381731],"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":[0.0007748476,0.0005093,0.1260105,0.0008612066,0.0003434763,0.0006571047,0.001587666,0.03600748,0.01708393,0.002948617,0.01635346,0.7968625],"study_design_scores_gemma":[0.00009434681,0.000341473,0.03806692,0.0002766367,0.000197067,0.0007227252,0.001131295,0.9113328,0.01578565,0.02046531,0.01148319,0.0001026004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2345765,0.001969599,0.7349318,0.006883372,0.0003566084,0.0009322994,0.004180524,0.01025662,0.005912643],"genre_scores_gemma":[0.5714593,0.0005522661,0.4218673,0.001029428,0.000292736,0.0002570503,0.003371728,0.000204161,0.0009660435],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009477912,"threshold_uncertainty_score":0.03230298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0731613636287202,"score_gpt":0.353814342137556,"score_spread":0.2806529785088359,"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."}}