{"id":"W3163233738","doi":"10.1093/bioinformatics/btab340","title":"CLEP: a hybrid data- and knowledge-driven framework for generating patient representations","year":2021,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Bundesministerium für Bildung und Forschung; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Computer science; Python (programming language); Embedding; Cluster analysis; Documentation; Machine learning; Domain knowledge; Artificial intelligence; Graph; Knowledge graph; Data mining; Theoretical computer science; Programming language","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003544273,0.001338733,0.0008630159,0.002509333,0.000653072,0.001982756,0.003096457,0.001944125,0.01861716],"category_scores_gemma":[0.01794072,0.0007672788,0.002403983,0.001847505,0.0008169588,0.001945271,0.004336267,0.00342858,0.006831353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001203335,"about_ca_system_score_gemma":0.003275952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005314221,"about_ca_topic_score_gemma":0.01220979,"domain_scores_codex":[0.9982483,0.0006904104,0.0001164493,0.0004593034,0.0003802898,0.0001051217],"domain_scores_gemma":[0.9952155,0.00279073,0.0002479274,0.0009262796,0.0005626376,0.0002569039],"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":[0.0007984551,0.0004095188,0.008712297,0.001178637,0.0004753887,0.00119096,0.0006344275,0.1214907,0.004047835,0.04655561,0.2308173,0.5836887],"study_design_scores_gemma":[0.0002341286,0.0001122999,0.001694772,0.0002215424,0.00007598457,0.0007779432,0.000146277,0.7520584,0.005463444,0.1612339,0.07787348,0.0001078751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002882394,0.0002312804,0.9491279,0.001426253,0.0001267136,0.0004466294,0.01502359,0.0287768,0.001958364],"genre_scores_gemma":[0.06275849,0.0003118946,0.8864262,0.001248472,0.0001231609,0.001293074,0.0417883,0.003297823,0.002752607],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01861716,"threshold_uncertainty_score":0.0622806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05388756787387535,"score_gpt":0.3534373595230252,"score_spread":0.2995497916491499,"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."}}