{"id":"W4398145086","doi":"10.1038/s41467-024-47557-1","title":"Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality","year":2024,"lang":"en","type":"article","venue":"Nature Communications","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; McGill University","funders":"National Research, Development and Innovation Office; Nemzeti Kutatási Fejlesztési és Innovációs Hivatal; Medical Research Council; Universität des Saarlandes; Innovációs és Technológiai Minisztérium; Ministère de la Santé; Public Health England; Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche; Public Health Agency; European Regional Development Fund; European Commission; Imperial College London; British Heart Foundation; Bundesministerium für Bildung und Forschung; National Institute for Health and Care Research; Ministère de la Santé et des Services sociaux; Ministero della Salute; National Institute for Health Research Health Protection Research Unit; Fonds National de la Recherche Luxembourg; Génome Québec; Universität Bielefeld; Public Health Agency of Canada; Deutsches Zentrum für Infektionsforschung","keywords":"Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; RNA; Virology; Long non-coding RNA; Computational biology; Computer science; Medicine; Biology; Genetics; Internal medicine; Outbreak; Gene","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001196713,0.0001590737,0.0002030887,0.0003868553,0.0003239523,0.0000965652,0.002528241,0.0001850319,0.000006402258],"category_scores_gemma":[0.0008678038,0.0001614067,0.00006511193,0.001179276,0.00004977092,0.0001999917,0.001088352,0.001982554,0.00000813857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004642201,"about_ca_system_score_gemma":0.001441445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002057323,"about_ca_topic_score_gemma":0.002747372,"domain_scores_codex":[0.9981972,0.0002700989,0.0004922571,0.000393404,0.0003921121,0.0002549227],"domain_scores_gemma":[0.99729,0.0005876501,0.0001076856,0.001704224,0.00009693186,0.0002135401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001547796,0.0005304919,0.3098516,0.0009831024,0.00009686645,0.00004329469,0.0340065,0.5068601,0.0004082061,0.09551501,0.0007601373,0.05092923],"study_design_scores_gemma":[0.0001285814,0.00003262175,0.01328445,0.0001696714,0.000005506637,0.000001924731,0.00003055434,0.9769606,0.00008477605,0.0001966125,0.008944502,0.0001601907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07267378,0.004673837,0.8944376,0.02524984,0.0003182658,0.0006627177,0.00002218932,0.0006933926,0.001268364],"genre_scores_gemma":[0.7107075,0.00002267693,0.2885998,0.0005089875,0.00001065314,0.00006625929,0.00004270126,0.00001297235,0.00002843024],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6380338,"threshold_uncertainty_score":0.8613322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0556219542794564,"score_gpt":0.3901864326260056,"score_spread":0.3345644783465491,"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."}}