{"id":"W3005283278","doi":"10.1038/s41746-020-0222-x","title":"Lymelight: forecasting Lyme disease risk using web search data","year":2020,"lang":"en","type":"article","venue":"npj Digital Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Lyme disease; Disease; Computer science; Machine learning; Psychological intervention; Web application; LYME; Medicine; Classifier (UML); Artificial intelligence; World Wide Web; Borrelia burgdorferi; Pathology; Psychiatry; Immunology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.002092694,0.0005425957,0.000541156,0.005372635,0.0002029055,0.001083133,0.0005966844,0.0007781279,0.001344314],"category_scores_gemma":[0.01099294,0.0001787934,0.0005757997,0.003065493,0.0001617249,0.001160247,0.0005678385,0.0005430529,0.000851611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006412663,"about_ca_system_score_gemma":0.0005211984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02126015,"about_ca_topic_score_gemma":0.01938058,"domain_scores_codex":[0.999126,0.0003963198,0.0000990997,0.0001707612,0.0001411772,0.0000666596],"domain_scores_gemma":[0.9936476,0.003967837,0.0009641445,0.0004232426,0.0006113108,0.0003857944],"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":[0.0005359572,0.0005725576,0.8467082,0.0004351898,0.0003527496,0.0001460672,0.0001488475,0.0645771,0.001058976,0.0008940193,0.01491739,0.06965297],"study_design_scores_gemma":[0.00005770325,0.0002762562,0.1581641,0.00009924929,0.00007752996,0.0001737453,0.0002516819,0.8342205,0.001135762,0.001580692,0.003929477,0.00003333626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9560091,0.001761239,0.00795401,0.001903031,0.0001052376,0.0002091272,0.02847051,0.001275176,0.002312554],"genre_scores_gemma":[0.9623478,0.0004555649,0.01409687,0.0001497603,0.0001042254,0.0000755119,0.02209067,0.00002674969,0.0006528093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02126015,"threshold_uncertainty_score":0.04227281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1929112390497841,"score_gpt":0.3394312484706375,"score_spread":0.1465200094208534,"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."}}