{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008911224,0.0001134675,0.0003802052,0.00007509209,0.001046242,0.00004522558,0.0002855225,0.0001864159,0.00003457897],"category_scores_gemma":[0.007646004,0.000136585,0.0002065496,0.0006553179,0.0001537937,0.0006423244,0.0001273744,0.000376468,0.000004108547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000515657,"about_ca_system_score_gemma":0.001993839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004187576,"about_ca_topic_score_gemma":0.0007099188,"domain_scores_codex":[0.9941732,0.002613896,0.000358795,0.0003828643,0.0021203,0.0003509465],"domain_scores_gemma":[0.9973641,0.000836433,0.0002743061,0.0004369132,0.0009843595,0.00010389],"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":[0.00001784422,0.00006577353,0.3276725,0.0001598271,0.0005702435,0.000002981549,0.2403104,0.06780487,0.00004673491,0.0002817968,0.0001014861,0.3629654],"study_design_scores_gemma":[0.001539376,0.00002419693,0.1745201,0.0002075892,0.002269422,0.0000058646,0.05719126,0.7440946,0.0002021941,0.0007752484,0.01855511,0.0006151135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885046,0.004022203,0.0002765318,0.00007764346,0.003374938,0.0007880192,0.00004678265,0.0000683303,0.00284098],"genre_scores_gemma":[0.9945467,0.0007654827,0.0006899704,0.000006826295,0.003679388,0.0001754695,0.0001095741,0.00002256605,0.000003999318],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6762897,"threshold_uncertainty_score":0.9153534,"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."}}