{"id":"W2588815870","doi":"10.1109/ssci.2016.7849886","title":"Using machine learning to predict hypertension from a clinical dataset","year":2016,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Indian Institute of Technology Bombay","keywords":"Artificial neural network; Computer science; Machine learning; Artificial intelligence; Demographics; Disease; Coronary artery disease; Warning system; Risk factor; Medicine; Internal 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":[0.001314674,0.0008344837,0.0005922201,0.002152808,0.0003467934,0.0006484298,0.0007280456,0.0009259066,0.0009614707],"category_scores_gemma":[0.006780985,0.0001987904,0.0005352533,0.001894819,0.0002335762,0.0005016508,0.0004706126,0.001001962,0.0005737377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001111014,"about_ca_system_score_gemma":0.001200233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03543345,"about_ca_topic_score_gemma":0.04979182,"domain_scores_codex":[0.9992998,0.0001990408,0.00008220626,0.0001704614,0.000166417,0.0000821539],"domain_scores_gemma":[0.9965793,0.002123406,0.0002396494,0.0003432016,0.0005575392,0.0001568874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001551906,0.002865631,0.4862201,0.0005741824,0.00101515,0.001723322,0.0002245279,0.2056963,0.004730135,0.0008298118,0.02778167,0.2667873],"study_design_scores_gemma":[0.000112648,0.0004764103,0.109104,0.00005759307,0.0001237653,0.0003612037,0.0001683532,0.880363,0.003042419,0.00172218,0.004422584,0.0000457792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9240941,0.00119626,0.0303034,0.001634916,0.0002730842,0.0004437724,0.03775411,0.001510649,0.002789733],"genre_scores_gemma":[0.9009752,0.0004374957,0.03303827,0.0002363935,0.0001509208,0.0002607692,0.06381039,0.00002902516,0.001061607],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03543345,"threshold_uncertainty_score":0.07045442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1323618872952212,"score_gpt":0.3877024547970157,"score_spread":0.2553405675017945,"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."}}