{"id":"W2954286747","doi":"10.1371/journal.pone.0218760","title":"Feature selection and transformation by machine learning reduce variable numbers and improve prediction for heart failure readmission or death","year":2019,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Heart Failure Treatment and Management","field":"Medicine","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Department of Health, Government of Western Australia","keywords":"Feature selection; Receiver operating characteristic; Predictive modelling; Medicine; Multilayer perceptron; Heart failure; Machine learning; Artificial intelligence; Perceptron; Statistics; Computer science; Artificial neural network; Internal medicine; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001588265,0.0001229187,0.0002120032,0.00005905444,0.0001153851,0.00002953577,0.00001217984,0.0001134362,0.00006677691],"category_scores_gemma":[0.00004184472,0.00009394869,0.00002025315,0.00009183476,0.000008849543,0.000221302,0.000008366682,0.0001608531,0.000005260623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006045614,"about_ca_system_score_gemma":0.00002788181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007188074,"about_ca_topic_score_gemma":0.00001817815,"domain_scores_codex":[0.9993134,0.00002359792,0.0001116674,0.0002261624,0.0001615766,0.0001635628],"domain_scores_gemma":[0.9996933,0.00003372326,0.00003815635,0.00007237327,0.00005482551,0.0001076036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002362092,0.0009785747,0.04645952,0.002338188,0.0006314093,9.957503e-7,0.0008240144,0.000007862883,0.9148816,0.0002678436,0.0277604,0.003487533],"study_design_scores_gemma":[0.02117519,0.01264926,0.00537694,0.00207074,0.003219869,0.0001093666,0.001073473,0.07729114,0.2565571,0.0001560214,0.6196195,0.0007014017],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9721076,0.0002664579,0.001327457,0.02222722,0.00004927373,0.003153699,0.0000401496,0.000208786,0.0006193861],"genre_scores_gemma":[0.9112155,0.000357733,0.04945721,0.0002713788,0.0001567367,0.0001910842,0.0007618716,0.00004713939,0.03754133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6583245,"threshold_uncertainty_score":0.3831117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0153443060725264,"score_gpt":0.2364064399268496,"score_spread":0.2210621338543232,"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."}}