{"id":"W4400578665","doi":"10.3390/bioengineering11070709","title":"Two-Stream Convolutional Neural Networks for Breathing Pattern Classification: Real-Time Monitoring of Respiratory Disease Patients","year":2024,"lang":"en","type":"article","venue":"Bioengineering","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"International Collaboration On Repair Discoveries; University of British Columbia","funders":"National Institutes of Health","keywords":"Convolutional neural network; Pattern recognition (psychology); Classifier (UML); Computer science; Artificial intelligence; Autoencoder; Random forest; Artificial neural network; Feature (linguistics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000148706,0.0001711788,0.0002112008,0.0001742675,0.00005046507,0.00003629137,0.0000811835,0.00006155002,0.00001242724],"category_scores_gemma":[0.0001245618,0.0001760067,0.0001401672,0.0001995552,0.0000357836,0.0001165775,0.0000372337,0.0001312569,0.000005333026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002417429,"about_ca_system_score_gemma":0.00009060986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002353035,"about_ca_topic_score_gemma":2.455062e-7,"domain_scores_codex":[0.9988561,0.00001413849,0.0003225685,0.0003167108,0.0002398173,0.0002507162],"domain_scores_gemma":[0.9990935,0.000289226,0.00005217102,0.0002592116,0.0001267217,0.0001791739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003215495,0.00057149,0.7563618,0.00472735,0.0004672247,0.0001476014,0.0004637687,0.1289513,0.0334462,0.0006163749,0.005954504,0.06797083],"study_design_scores_gemma":[0.0007881564,0.0001064747,0.2457125,0.001081136,0.0001541245,0.000002285307,0.00001186635,0.7494531,0.000555842,0.000005027692,0.001948141,0.0001813508],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9739331,0.002046973,0.01914666,0.001585001,0.001773929,0.0007619307,0.0001215118,0.0006013913,0.00002950114],"genre_scores_gemma":[0.9977957,0.00002593513,0.0007285518,0.0001719485,0.001007581,0.00009171686,0.00008446021,0.00005773337,0.00003635885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6205018,"threshold_uncertainty_score":0.7177345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03273320349353004,"score_gpt":0.3035277914150019,"score_spread":0.2707945879214718,"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."}}