{"id":"W4281679558","doi":"10.1038/s41598-022-13015-5","title":"Empirical analyses and simulations showed that different machine and statistical learning methods had differing performance for predicting blood pressure","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"National Center for Advancing Translational Sciences; Canadian Institutes of Health Research; National Institutes of Health; Georgia Clinical and Translational Science Alliance; Heart and Stroke Foundation of Canada","keywords":"Random forest; Machine learning; Artificial intelligence; Lasso (programming language); Regression; Artificial neural network; Computer science; Decision tree; Gradient boosting; Ordinary least squares; Linear regression; Range (aeronautics); Ensemble learning; Statistics; Mathematics; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002194325,0.000189934,0.000297723,0.0002163743,0.002341025,0.000535112,0.0002452251,0.00004205753,0.00004119682],"category_scores_gemma":[0.001273287,0.0001669832,0.00004737558,0.0003477329,0.0001302435,0.0002500935,0.0009117518,0.0005067778,9.450665e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002410494,"about_ca_system_score_gemma":0.00006474955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002624866,"about_ca_topic_score_gemma":0.00001090986,"domain_scores_codex":[0.9970062,0.0004656743,0.0004647514,0.001078131,0.000584499,0.0004007749],"domain_scores_gemma":[0.9973514,0.001491416,0.0003057594,0.000574793,0.00008279865,0.0001938349],"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":[0.000006406091,0.00006468582,0.9552063,0.0001815133,0.00004419826,0.00002653465,0.001573151,0.02627444,0.001961839,0.00007832274,0.00005099806,0.01453161],"study_design_scores_gemma":[0.0001590199,0.0001628314,0.124612,0.00001659369,0.0000858917,0.00023172,0.00005411837,0.8703909,0.0006372441,0.0007376024,0.002750335,0.0001617272],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6776208,0.0006463249,0.3196596,0.0003114513,0.001124862,0.0004155909,0.00001541788,0.0001742712,0.00003162458],"genre_scores_gemma":[0.935054,0.000003706974,0.06419452,0.00002412714,0.00003266137,0.00007114263,0.00004653424,0.00001622615,0.0005570606],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8441165,"threshold_uncertainty_score":0.9989578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08198910350752049,"score_gpt":0.4119931283913835,"score_spread":0.330004024883863,"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."}}