{"id":"W4387185802","doi":"10.24846/v32i3y202309","title":"Deep Learning Model for Early Subsequent COPD Exacerbation Prediction","year":2023,"lang":"en","type":"article","venue":"Studies in Informatics and Control","topic":"Chronic Obstructive Pulmonary Disease (COPD) Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre; University of Twente; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Computer science; Exacerbation; Copd exacerbation; COPD; Artificial intelligence; Deep learning; Machine learning; Medicine; Internal medicine; Acute exacerbation of chronic obstructive pulmonary disease","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":[0.0009219733,0.000835041,0.0006607393,0.0006000649,0.0002436304,0.0005580662,0.0007371756,0.0007187613,0.001473429],"category_scores_gemma":[0.002049481,0.0002858219,0.0006113811,0.0004470009,0.0001622576,0.0006228535,0.0006201929,0.00134727,0.0003859769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000718118,"about_ca_system_score_gemma":0.001171029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01464718,"about_ca_topic_score_gemma":0.01170261,"domain_scores_codex":[0.9997268,0.0000569777,0.00003327914,0.00007296572,0.00004770077,0.00006234117],"domain_scores_gemma":[0.9994288,0.0002994069,0.0000507176,0.00002989725,0.0001656192,0.00002546697],"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":[0.000507916,0.0004752265,0.01353518,0.0001279028,0.0001716185,0.0002025053,0.00008152443,0.7447042,0.003565061,0.001154654,0.0046487,0.2308254],"study_design_scores_gemma":[0.000005078262,0.00003291877,0.0005732551,0.000006475812,0.00001034545,0.000008920765,0.000004868752,0.9983485,0.0004289555,0.0004430125,0.0001342081,0.000003559909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.49916,0.004518454,0.4848107,0.001782805,0.0004535853,0.0001605156,0.00169875,0.002742654,0.004672447],"genre_scores_gemma":[0.9727076,0.0004576916,0.02260269,0.0001886522,0.00005158478,0.0001014098,0.001094495,0.00002438634,0.002771453],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01464718,"threshold_uncertainty_score":0.02912384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03649568052189048,"score_gpt":0.3210129474417679,"score_spread":0.2845172669198774,"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."}}