{"id":"W3081455901","doi":"10.1101/2020.08.20.259226","title":"CLEP: A Hybrid Data- and Knowledge-Driven Framework for Generating Patient Representations","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"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; Knowledge graph; Cluster analysis; Artificial intelligence; Machine learning; Embedding; Python (programming language); Variety (cybernetics); Graph; Documentation; Theoretical computer science","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":[0.004346402,0.001173545,0.0009620811,0.002778189,0.0006086421,0.001883483,0.002734907,0.001995179,0.009887666],"category_scores_gemma":[0.01689845,0.0007065571,0.001973315,0.001921448,0.000879965,0.001671782,0.003339347,0.00324246,0.003364509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001134358,"about_ca_system_score_gemma":0.002525014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004158334,"about_ca_topic_score_gemma":0.008095637,"domain_scores_codex":[0.9980168,0.000920134,0.0001114989,0.0004466417,0.0004092201,0.00009558554],"domain_scores_gemma":[0.9936434,0.004010549,0.0002938474,0.001120359,0.000667006,0.0002648169],"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":[0.0007941,0.0005254342,0.007056378,0.000888541,0.0004479972,0.00108626,0.0004500252,0.2099615,0.005645534,0.04280195,0.1267534,0.6035889],"study_design_scores_gemma":[0.0001404391,0.00007665154,0.0008329914,0.000100015,0.00004086082,0.0004133706,0.00007001888,0.8788035,0.004785523,0.09066454,0.02401237,0.00005976696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003010391,0.000158026,0.9764417,0.0009047633,0.00007435789,0.000311014,0.005555095,0.01260027,0.0009443486],"genre_scores_gemma":[0.06641781,0.0002164488,0.9116256,0.0008960103,0.0001093618,0.0008354738,0.0169344,0.001486767,0.001478208],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009887666,"threshold_uncertainty_score":0.03307754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04579274490232325,"score_gpt":0.3097760788648998,"score_spread":0.2639833339625765,"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."}}