{"id":"W4360610210","doi":"10.2196/preprints.47014","title":"Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis (Preprint)","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Lyme disease; Artificial intelligence; LYME; Computer science; Machine learning; Preprocessor; Natural language processing; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005441285,0.0007747341,0.0003564101,0.000736572,0.0002050022,0.0005637903,0.0005082238,0.0005370173,0.001437453],"category_scores_gemma":[0.00129662,0.0002459328,0.0007092684,0.0003447498,0.0001369929,0.0005552674,0.0004971118,0.0008397343,0.000778663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004010172,"about_ca_system_score_gemma":0.0003140162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00646102,"about_ca_topic_score_gemma":0.008249894,"domain_scores_codex":[0.9998786,0.00002955443,0.00001046087,0.00003620587,0.0000160792,0.00002897534],"domain_scores_gemma":[0.9996762,0.0001620828,0.00003667491,0.00001794948,0.00008434107,0.00002272231],"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":[0.001340102,0.001254955,0.09453419,0.0004122063,0.0005143801,0.0008195501,0.0006077762,0.39439,0.03367959,0.001486719,0.02876413,0.4421964],"study_design_scores_gemma":[0.00000564152,0.00004121401,0.003105591,0.000009209674,0.0000215423,0.00001779298,0.00004373206,0.994187,0.001817514,0.0003473195,0.0003966021,0.000006924896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7973152,0.000991333,0.1871865,0.001781301,0.0004633101,0.0001942177,0.004656929,0.003273514,0.00413773],"genre_scores_gemma":[0.946549,0.0003106303,0.04477768,0.0002402554,0.0001362799,0.0001174085,0.004224275,0.00006966459,0.003574813],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00646102,"threshold_uncertainty_score":0.01284683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08955540384627714,"score_gpt":0.3534182521350295,"score_spread":0.2638628482887524,"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."}}