{"id":"W236448318","doi":"10.1371/journal.pone.0127428","title":"Personalized Mortality Prediction Driven by Electronic Medical Data and a Patient Similarity Metric","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":168,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; University of Waterloo","funders":"University of Waterloo; Natural Sciences and Engineering Research Council of Canada; Queen's University","keywords":"Metric (unit); Similarity (geometry); Personalized medicine; Computational biology; Computer science; Bioinformatics; Medicine; Biology; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001650921,0.0006389365,0.0007893122,0.001825489,0.0002033413,0.0007363012,0.0007264183,0.0006108129,0.0005419484],"category_scores_gemma":[0.006492236,0.0001895491,0.0005352846,0.001435927,0.0002189574,0.0008053918,0.0007735268,0.0007610223,0.0001985171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006944272,"about_ca_system_score_gemma":0.0005283316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003908689,"about_ca_topic_score_gemma":0.004641584,"domain_scores_codex":[0.999088,0.0002826191,0.0001003789,0.0002636527,0.0001860741,0.00007922588],"domain_scores_gemma":[0.9961706,0.00222769,0.0006242844,0.0003355482,0.0004192534,0.0002227069],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00122557,0.00114977,0.31035,0.000199799,0.0003572725,0.0007730304,0.0002150014,0.4582337,0.005295936,0.001652004,0.004135816,0.2164121],"study_design_scores_gemma":[0.00001498461,0.0001297687,0.0227642,0.00000705718,0.00002375417,0.0001166513,0.00003911198,0.974251,0.001335053,0.00105581,0.000250385,0.00001225394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8960548,0.0004050057,0.09920964,0.0005992884,0.00006526506,0.0001421374,0.001968296,0.000688548,0.0008669653],"genre_scores_gemma":[0.9815718,0.00006230923,0.01650959,0.0000537483,0.00004137403,0.00004152202,0.001554969,0.000006855045,0.0001578547],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003908689,"threshold_uncertainty_score":0.008730948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09542917862554046,"score_gpt":0.3121350375905038,"score_spread":0.2167058589649633,"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."}}