{"id":"W2760560537","doi":"10.2196/iproc.8686","title":"Detecting Influenza Epidemics Using Self-reported Data Through Mobile App (FeverCoach)","year":2017,"lang":"en","type":"article","venue":"Iproceedings","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Public health surveillance; Public health; The Internet; Internet privacy; Health records; Mobile apps; Influenza-like illness; Lag time; Business; Environmental health; Medicine; Medical emergency; Computer science; Health care; Virology; World Wide Web; Biology; Political science; Nursing","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009916922,0.0003211256,0.0005988719,0.00007564397,0.0007476709,0.0002246595,0.001096511,0.0001823805,0.00005926785],"category_scores_gemma":[0.003682798,0.0003142894,0.0001010956,0.0001776495,0.0001720646,0.001885168,0.00121727,0.00042563,0.00008657807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001675432,"about_ca_system_score_gemma":0.0002199845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006464943,"about_ca_topic_score_gemma":0.00001296437,"domain_scores_codex":[0.9973097,0.0000149855,0.0006542028,0.0009303867,0.0005061493,0.0005845831],"domain_scores_gemma":[0.9961365,0.00008082645,0.0008406231,0.002309903,0.0003649872,0.0002671314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003337919,0.0003675187,0.9334008,0.0009843395,0.001035351,0.000348351,0.001875109,0.00002701872,0.03214756,0.000128776,0.01411119,0.0152402],"study_design_scores_gemma":[0.00999541,0.0004491739,0.1775679,0.002111204,0.003301235,0.002441538,0.002860432,0.07854838,0.01604259,0.001569415,0.7023508,0.002761972],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906501,0.0004165208,0.000424792,0.0001270667,0.0003444249,0.0006615038,0.0001633093,0.0007577134,0.006454627],"genre_scores_gemma":[0.9740848,0.00008017015,0.0234272,0.001312017,0.0007124778,0.00002567279,0.0001419503,0.0001012802,0.0001144089],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7558329,"threshold_uncertainty_score":0.9999309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1611207812623647,"score_gpt":0.4094856190534962,"score_spread":0.2483648377911314,"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."}}