{"id":"W4401023556","doi":"10.24963/ijcai.2024/890","title":"DiffECG: Diffusion Model-Powered Label-Efficient and Personalized Arrhythmia Diagnosis","year":2024,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":212,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Division of Chemistry; National Science Foundation","keywords":"Computer science; Programming language; Data science","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":[0.0001762507,0.0001551569,0.0001538474,0.000106241,0.0001038973,0.0003347883,0.0002826042,0.00006251137,0.00006450283],"category_scores_gemma":[0.00002157273,0.0001199876,0.00005235578,0.000206111,0.00003783141,0.0001729516,0.0003779918,0.0001241502,0.00002934752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004592717,"about_ca_system_score_gemma":0.00004001026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002676801,"about_ca_topic_score_gemma":0.000004115313,"domain_scores_codex":[0.9986508,0.00003172268,0.0001774578,0.0005796255,0.0002965872,0.0002638266],"domain_scores_gemma":[0.9993253,0.0001235726,0.00001853906,0.0003836548,0.00002720185,0.0001217327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008184582,0.0001032204,0.0004425476,0.0001344667,0.00003646449,0.0001220967,0.005048576,0.008250793,0.0021726,0.6962133,0.001254247,0.2862135],"study_design_scores_gemma":[0.0002556124,0.00002163402,0.00009238389,0.00006068218,0.000006755005,0.00001802462,0.00001850759,0.9940295,0.0002295686,0.003158689,0.001944965,0.0001636983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2282562,0.001538464,0.7649176,0.002691304,0.0003075147,0.0001415498,0.00000198879,0.0003683907,0.001777003],"genre_scores_gemma":[0.9407768,0.000181761,0.05624254,0.0002770707,0.00009100296,0.00005111771,9.016433e-7,0.00001349427,0.002365375],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9857787,"threshold_uncertainty_score":0.4892954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02283890185446718,"score_gpt":0.258264868383012,"score_spread":0.2354259665285449,"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."}}