{"id":"W3167778602","doi":"10.21428/594757db.ec72e2f4","title":"Patient representation learning from EHR data","year":2021,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Institut National de Santé Publique du Québec","keywords":"Polypharmacy; Health records; Electronic health record; Representation (politics); Data science; Computer science; Discipline; Medicine; Psychology; Health care; Pharmacology; Sociology","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":[0.002058028,0.000817703,0.0008211945,0.002250016,0.000283027,0.001506174,0.001121533,0.001125928,0.001824492],"category_scores_gemma":[0.01181196,0.0002638409,0.001022418,0.002442077,0.0003950278,0.001726104,0.00129714,0.002099357,0.0009054246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000926824,"about_ca_system_score_gemma":0.001192158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004393436,"about_ca_topic_score_gemma":0.005361938,"domain_scores_codex":[0.9985427,0.0006090649,0.0001032376,0.0003608433,0.0002459048,0.0001383666],"domain_scores_gemma":[0.994647,0.003730957,0.0003855469,0.000756858,0.0003650649,0.0001145178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006362579,0.001423198,0.05624622,0.0004266406,0.0004809588,0.000561022,0.0004120015,0.1561294,0.002865115,0.007052451,0.02645386,0.7473128],"study_design_scores_gemma":[0.00007684049,0.0002633355,0.007950864,0.0001239124,0.0001404977,0.0003069911,0.0002194789,0.9550105,0.002053594,0.02883979,0.004981592,0.00003266278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4511731,0.003605811,0.5013427,0.008818267,0.0004379567,0.0005502373,0.02334057,0.005153813,0.005577604],"genre_scores_gemma":[0.8719308,0.001023893,0.0988876,0.0006857736,0.0002793133,0.0003020906,0.02501155,0.00005202763,0.001827018],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004393436,"threshold_uncertainty_score":0.01088405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08399470855265054,"score_gpt":0.3479080320886982,"score_spread":0.2639133235360477,"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."}}