{"id":"W3171225007","doi":"10.1146/annurev-biodatasci-092820-033938","title":"Probabilistic Machine Learning for Healthcare","year":2021,"lang":"en","type":"review","venue":"PubMed Central","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Vector Institute","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institute for Advanced Research; National Heart, Lung, and Blood Institute; Microsoft Research","keywords":"Probabilistic logic; Machine learning; Computer science; Artificial intelligence; Generative grammar; Pipeline (software); Statistical model; Health care","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001408972,0.0006483329,0.002011699,0.0002162946,0.0003613141,0.0003615899,0.001756057,0.0004381076,0.00002014064],"category_scores_gemma":[0.002446803,0.0005811231,0.0008122157,0.0007940973,0.00004287212,0.0001790288,0.0005268946,0.001766562,0.00001972337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001078053,"about_ca_system_score_gemma":0.002019928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001454838,"about_ca_topic_score_gemma":0.00009309151,"domain_scores_codex":[0.992916,0.001150421,0.0009790835,0.001383511,0.000488996,0.003081955],"domain_scores_gemma":[0.9962668,0.0008672865,0.0006161449,0.001088941,0.0001712037,0.0009896705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001102158,0.00002885504,0.0001126002,0.03170964,0.000051766,0.0000273345,0.0001194986,0.00007655715,7.864947e-10,0.01723408,0.0002729955,0.9503656],"study_design_scores_gemma":[0.0001497989,0.00005766539,0.00007303067,0.003236532,0.0001068598,0.0001224787,0.000002914608,0.006082734,4.024815e-8,0.0002122164,0.9894403,0.0005154695],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[3.504258e-7,0.9718683,0.02016414,0.001251743,0.002778705,0.003297641,0.00005266485,0.0004788557,0.0001075931],"genre_scores_gemma":[0.00001350664,0.9852309,0.009003024,0.0001940278,0.001083913,0.00283073,0.000622156,0.0001075598,0.0009141885],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9891673,"threshold_uncertainty_score":0.999664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07213291695732811,"score_gpt":0.3382493987669312,"score_spread":0.2661164818096031,"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."}}