{"id":"W2883079412","doi":"10.2196/publichealth.9022","title":"Issues in Building a Nursing Home Syndromic Surveillance System with Textmining: Longitudinal Observational Study","year":2018,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Cohort; Minimum Data Set; Public health surveillance; Public health; Medical emergency; Cohort study; Population; Long-term care; Database; Family medicine; Environmental health; Pediatrics; Nursing homes; Computer science; Nursing; Internal medicine","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":[0.03717672,0.0004446208,0.000499303,0.001128067,0.001136522,0.00164231,0.001022003,0.0008426961,0.0008331656],"category_scores_gemma":[0.04490127,0.0005325061,0.001020294,0.001613662,0.0006804227,0.003170501,0.001638853,0.001005872,0.0002998721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00133512,"about_ca_system_score_gemma":0.003140309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0217007,"about_ca_topic_score_gemma":0.02230911,"domain_scores_codex":[0.9775586,0.01550768,0.00225574,0.00125458,0.002282328,0.001141079],"domain_scores_gemma":[0.9780588,0.007649588,0.00463422,0.003106876,0.005184068,0.001366388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001051891,0.0003628057,0.9946911,0.00003700075,0.00005547144,0.00006355599,0.001467306,0.00005928303,0.00006335768,0.00005862747,0.0003068349,0.00272946],"study_design_scores_gemma":[0.0000560027,0.002100259,0.9807022,0.0002022127,0.0001599167,0.0002989229,0.009724801,0.003909149,0.0003412699,0.0002327474,0.002234983,0.00003758621],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959713,0.0002044009,0.002308314,0.0002814421,0.00002277726,0.0002406709,0.0004971153,0.00001311354,0.0004608214],"genre_scores_gemma":[0.9950442,0.0001180153,0.003165753,0.0002198397,0.00002832081,0.0004824529,0.000757519,0.0000106216,0.0001732333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03717672,"threshold_uncertainty_score":0.1966116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08159101760074638,"score_gpt":0.3755431428251311,"score_spread":0.2939521252243847,"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."}}