{"id":"W4322719003","doi":"10.1177/14604582221136712","title":"Identifying adverse drug reactions from patient reviews on social media using natural language processing","year":2023,"lang":"en","type":"article","venue":"Health Informatics Journal","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Pharmacovigilance; Drug reaction; Social media; Medicine; Adverse drug reaction; Drug; Globe; Health professionals; Public health; Medical emergency; Pharmacology; Health care; Computer science; Nursing; Political science; World Wide Web; Ophthalmology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.003431167,0.001296372,0.0008445418,0.009544471,0.0005846566,0.001912959,0.0007704899,0.0009419018,0.001333916],"category_scores_gemma":[0.01554931,0.0003114259,0.001167593,0.003305074,0.0003525493,0.001575742,0.0009713586,0.0007730287,0.0009619935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007132184,"about_ca_system_score_gemma":0.001229432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003667751,"about_ca_topic_score_gemma":0.01154416,"domain_scores_codex":[0.99447,0.001752637,0.001098565,0.000949476,0.001558093,0.0001711426],"domain_scores_gemma":[0.9696571,0.02004889,0.00564228,0.001032076,0.003308044,0.0003117061],"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":[0.001213,0.001276519,0.1343547,0.007922448,0.0009650201,0.008061583,0.00356755,0.006621833,0.07501925,0.002171933,0.04346299,0.7153631],"study_design_scores_gemma":[0.000258558,0.001572168,0.4089506,0.00158489,0.00129062,0.01286336,0.0063879,0.3236661,0.08640966,0.01383299,0.1426197,0.0005634048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6144399,0.01166086,0.2175739,0.006222707,0.001001272,0.005494263,0.1192784,0.009033171,0.01529549],"genre_scores_gemma":[0.6426223,0.002780165,0.2880444,0.001207058,0.0009550888,0.001622895,0.05877863,0.0001745058,0.003815101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009544471,"threshold_uncertainty_score":0.01814598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2917020809598433,"score_gpt":0.5211036278481078,"score_spread":0.2294015468882645,"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."}}