{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002520032,0.0009246098,0.001461455,0.002365561,0.000367479,0.001821996,0.001135244,0.002686355,0.008375612],"category_scores_gemma":[0.008099884,0.0003940723,0.001113084,0.002548382,0.001367751,0.002522291,0.00128688,0.004184826,0.003572973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001740967,"about_ca_system_score_gemma":0.002596976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002049965,"about_ca_topic_score_gemma":0.001850373,"domain_scores_codex":[0.9988089,0.0005139906,0.00009870365,0.0001425487,0.0003837846,0.00005212943],"domain_scores_gemma":[0.9960659,0.003146137,0.0001775517,0.0001377564,0.0003916822,0.00008101896],"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":[0.00005217078,0.00004878329,0.0003170031,0.01495775,0.0002289417,0.000149189,0.00009722267,0.004038513,0.000291797,0.08983786,0.09119467,0.7987861],"study_design_scores_gemma":[0.00002534842,0.00006512793,0.0006656594,0.01032056,0.0001101954,0.0007343814,0.00006412273,0.002294668,0.0002990068,0.1489591,0.8364037,0.00005817493],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001189684,0.9852279,0.005764248,0.004475422,0.0008116797,0.00001814019,0.00009369567,0.00005250636,0.003437348],"genre_scores_gemma":[0.005749638,0.985826,0.003681688,0.001775092,0.0012979,0.00004918448,0.0001586726,0.00002138603,0.001440395],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.008375612,"threshold_uncertainty_score":0.02801925,"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."}}