{"id":"W4406748510","doi":"10.1038/s41598-025-85722-8","title":"A noninvasive hyperkalemia monitoring system for dialysis patients based on a 1D-CNN model and single-lead ECG from wearable devices","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advancements in Battery Materials","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"West China Hospital, Sichuan University; Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperkalemia; Computer science; Convolutional neural network; Wearable computer; Sensitivity (control systems); Channel (broadcasting); Artificial intelligence; Preprocessor; Stability (learning theory); Real-time computing; Pattern recognition (psychology); Medicine; Machine learning; Embedded system; Internal medicine; Engineering; Electronic engineering","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":[0.0001370096,0.0004803918,0.0003640418,0.0001938502,0.000136469,0.0003229136,0.0004686784,0.0004432227,0.001210379],"category_scores_gemma":[0.0003019578,0.0002194665,0.0004610843,0.0001818205,0.00008428312,0.0003777472,0.0003686694,0.0003004235,0.0003339651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003817986,"about_ca_system_score_gemma":0.0004109584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00479053,"about_ca_topic_score_gemma":0.008197926,"domain_scores_codex":[0.9999089,0.000009402733,0.000004974333,0.00003493862,0.00002831767,0.0000133597],"domain_scores_gemma":[0.9999481,0.00001309544,0.000007842085,0.00000588026,0.00001878202,0.000006192707],"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":[0.0008238779,0.0003898672,0.02241173,0.0004117351,0.0003280755,0.00140138,0.000200082,0.2676483,0.2090853,0.001536119,0.005885609,0.4898781],"study_design_scores_gemma":[0.00001567885,0.0001373335,0.005120909,0.00001733608,0.00005763267,0.0002164316,0.00001350108,0.9813098,0.01155389,0.0002768675,0.00126183,0.00001883412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2720609,0.00116877,0.7167897,0.0005569159,0.0002300992,0.0001729054,0.0005040852,0.003147251,0.005369405],"genre_scores_gemma":[0.9253716,0.0006191265,0.06883328,0.0002823721,0.00004157957,0.0001465062,0.0004251874,0.0000438005,0.004236574],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00479053,"threshold_uncertainty_score":0.009525299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01561813164680342,"score_gpt":0.2320602647139582,"score_spread":0.2164421330671548,"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."}}