{"id":"W3125804658","doi":"10.2196/24473","title":"Predicting Cardiovascular Risk Using Social Media Data: Performance Evaluation of Machine-Learning Models","year":2021,"lang":"en","type":"article","venue":"JMIR Cardio","topic":"Social Media in Health Education","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Atherosclerotic cardiovascular disease; Cohort; Medicine; Artificial intelligence; Framingham Risk Score; Social media; Medical record; Predictive power; Machine learning; Emergency department; Disease; Internal medicine; Computer science; World Wide Web; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02167766,0.002432219,0.001622235,0.00444501,0.0006653909,0.002145695,0.001352146,0.002233381,0.0009058612],"category_scores_gemma":[0.03432592,0.0003733828,0.001441366,0.00179513,0.0008019285,0.002288067,0.001762615,0.001708045,0.0006557185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001193629,"about_ca_system_score_gemma":0.0008942253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008360964,"about_ca_topic_score_gemma":0.004175256,"domain_scores_codex":[0.9932705,0.004020215,0.0005579763,0.001037957,0.0008332881,0.0002801531],"domain_scores_gemma":[0.9470987,0.04491621,0.002082014,0.001855918,0.00311936,0.0009277004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002476578,0.002004825,0.5460111,0.0003561163,0.002210874,0.0002336942,0.0002812537,0.2703712,0.0008972061,0.0007933727,0.004603664,0.1697601],"study_design_scores_gemma":[0.00004702693,0.0004499396,0.02239265,0.00003699436,0.0001022913,0.00008322232,0.00007375142,0.9752418,0.0004699042,0.0007896546,0.0002850692,0.00002760735],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9528427,0.002965026,0.03470634,0.001796096,0.0003677838,0.0003418452,0.002326561,0.001134653,0.003518904],"genre_scores_gemma":[0.9853365,0.0003563302,0.01212952,0.0001299988,0.0001397387,0.00009483309,0.001422801,0.00003585531,0.0003544138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02167766,"threshold_uncertainty_score":0.1146438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3098486019007682,"score_gpt":0.4209840949848513,"score_spread":0.111135493084083,"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."}}