{"id":"W4223597069","doi":"10.2196/37771","title":"Predicting COVID-19 Symptoms From Free Text in Medical Records Using Artificial Intelligence: Feasibility Study","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universiteit Antwerpen","keywords":"Computer science; Artificial intelligence; Categorization; USable; Machine learning; Classifier (UML); Relevance (law); Coding (social sciences); Binary classification; Medical record; Natural language processing; Information retrieval; Data science; Medicine; World Wide Web; Support vector machine","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":[0.00606006,0.000671086,0.0004851523,0.00136504,0.0003025162,0.0007558989,0.0009548895,0.001058022,0.001325537],"category_scores_gemma":[0.02114101,0.0003020856,0.0007334918,0.0009916348,0.0004474907,0.001361002,0.000797317,0.0006700014,0.0004101627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009071814,"about_ca_system_score_gemma":0.001198177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006868782,"about_ca_topic_score_gemma":0.003378072,"domain_scores_codex":[0.9964696,0.002204379,0.000269307,0.0004420746,0.0004628587,0.0001518148],"domain_scores_gemma":[0.9801977,0.01627354,0.0007302499,0.0008921085,0.001513454,0.0003929148],"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":[0.009260091,0.02197638,0.5910519,0.0008962292,0.0005326286,0.001283937,0.001219607,0.08542697,0.009152743,0.0006866734,0.002445693,0.2760672],"study_design_scores_gemma":[0.000731168,0.01039546,0.1726247,0.00007351916,0.0002362526,0.0004107759,0.0009767337,0.8056241,0.006983657,0.0009569563,0.000909718,0.00007685814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912572,0.00005958065,0.007000049,0.0001453629,0.00001144896,0.0005704766,0.0005042335,0.00009222856,0.0003593039],"genre_scores_gemma":[0.981429,0.00005470826,0.01652035,0.00004520576,0.00001711142,0.0004149934,0.001281191,0.00000561047,0.0002319395],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006868782,"threshold_uncertainty_score":0.03204906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0768163983984464,"score_gpt":0.4045378472332662,"score_spread":0.3277214488348198,"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."}}