Predicting dry weight change in Hemodialysis patients using machine learning
暂无分享,去创建一个
Eiryo Kawakami | K. Asanuma | N. Hattori | M. Aizawa | Satoshi Suzuki | Takayuki Fujii | N. Tatsumoto | Megumi Oya | Hanae Wakabayashi | Hiroko Inoue | Kyogo Wagatsuma | Masatomo Kamimae | Yusuke Kashiwagi | Masayoshi Ishii
[1] S. Yoshimura,et al. Development of Machine Learning Models to Predict Probabilities and Types of Stroke at Prehospital Stage: the Japan Urgent Stroke Triage Score Using Machine Learning (JUST-ML) , 2021, Translational Stroke Research.
[2] B. Thapa-Chhetry,et al. Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care , 2021, Critical Care.
[3] K. Na,et al. A novel approach to dry weight adjustments for dialysis patients using machine learning , 2021, PloS one.
[4] Chenyang Lu,et al. Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications , 2021, JAMA network open.
[5] Miguel Vargas-Lombardo,et al. Predicting the Appearance of Hypotension during Hemodialysis Sessions Using Machine Learning Classifiers , 2021, International journal of environmental research and public health.
[6] Yijie Ding,et al. Assessing Dry Weight of Hemodialysis Patients via Sparse Laplacian Regularized RVFL Neural Network with L2,1-Norm , 2021, BioMed research international.
[7] K. Oh,et al. Machine learning model to predict hypotension after starting continuous renal replacement therapy , 2020, Scientific Reports.
[8] D. Evans,et al. The Use of Visceral Proteins as Nutrition Markers: An ASPEN Position Paper. , 2020, Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition.
[9] Yoshiki Sugitani,et al. Artificial intelligence supported anemia control system (AISACS) to prevent anemia in maintenance hemodialysis patients , 2020, International journal of medical sciences.
[10] V. W. Anelli,et al. Development and testing of an artificial intelligence tool for predicting end stage kidney disease in patients with immunoglobulin A nephropathy. , 2020, Kidney international.
[11] K. Carey,et al. Internal and External Validation of a Machine Learning Risk Score for Acute Kidney Injury , 2020, JAMA network open.
[12] Carlo Barbieri,et al. Enhanced prediction of hemoglobin concentration in a very large cohort of hemodialysis patients by means of deep recurrent neural networks , 2020, Artif. Intell. Medicine.
[13] W. Ju,et al. Machine‐learning–based early prediction of end‐stage renal disease in patients with diabetic kidney disease using clinical trials data , 2020, Diabetes, obesity & metabolism.
[14] David W. Johnson,et al. Pathophysiology and Significance of Natriuretic Peptides in Patients with End-stage Kidney Disease. , 2020, Clinical biochemistry.
[15] Pei-Yu Wu,et al. Predictive modeling of blood pressure during hemodialysis: a comparison of linear model, random forest, support vector regression, XGBoost, LASSO regression and ensemble method , 2020, Comput. Methods Programs Biomed..
[16] Finnian R. Mc Causland,et al. Predictors of Intradialytic Symptoms: An Analysis of Data From the Hemodialysis Study. , 2020, American journal of kidney diseases : the official journal of the National Kidney Foundation.
[17] M. Joseph,et al. Management of heart failure in patients with end-stage kidney disease on maintenance dialysis: a practical guide. , 2020, Reviews in cardiovascular medicine.
[18] Li Yang,et al. Study of cardiovascular disease prediction model based on random forest in eastern China , 2020, Scientific Reports.
[19] B. Canaud,et al. Sodium, volume and pressure control in haemodialysis patients for improved cardiovascular outcomes , 2020, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.
[20] Donald E. Brown,et al. A recurrent neural network approach to predicting hemoglobin trajectories in patients with End-Stage Renal Disease , 2020, Artif. Intell. Medicine.
[21] Jayoun Kim,et al. Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy , 2020, Critical Care.
[22] Xiaohang Wu,et al. A practical model for the identification of congenital cataracts using machine learning , 2020, EBioMedicine.
[23] M. Tapolyai,et al. The association of overhydration with chronic inflammation in chronic maintenance hemodiafiltration patients , 2019, Hemodialysis international. International Symposium on Home Hemodialysis.
[24] Jason S. Shapiro,et al. Application of Artificial Intelligence for Preoperative Diagnostic and Prognostic Prediction in Epithelial Ovarian Cancer Based on Blood Biomarkers , 2019, Clinical Cancer Research.
[25] P. Stenvinkel,et al. Chronic inflammation in end-stage renal disease and dialysis , 2018, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.
[26] N. Joki,et al. Dry weight targeting: The art and science of conventional hemodialysis , 2018, Seminars in dialysis.
[27] K. Kalantar-Zadeh,et al. A brief review of intradialytic hypotension with a focus on survival , 2017, Seminars in dialysis.
[28] Yuedong Wang,et al. Impact of fluid status and inflammation and their interaction on survival: a study in an international hemodialysis patient cohort. , 2017, Kidney international.
[29] Amjad Khan,et al. Management of Patient Care in Hemodialysis While Focusing on Cardiovascular Disease Events and the Atypical Role of Hyper- and/or Hypotension: A Systematic Review , 2016, BioMed research international.
[30] B. Canaud,et al. An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients. , 2016, Kidney international.
[31] K. Borgwardt,et al. Machine Learning in Medicine , 2015, Mach. Learn. under Resour. Constraints Vol. 3.
[32] M. Sampaolesi,et al. Role of Inflammation in Muscle Homeostasis and Myogenesis , 2015, Mediators of inflammation.
[33] H. Hirakata,et al. Japanese Society for Dialysis Therapy Guidelines for Management of Cardiovascular Diseases in Patients on Chronic Hemodialysis , 2012, Therapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy.
[34] K. Tabei,et al. Japanese Society for Dialysis Therapy Guidelines for Management of Cardiovascular Diseases in Patients on Chronic Hemodialysis , 2012, Therapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy.
[35] Melody A Swartz,et al. Interstitial fluid and lymph formation and transport: physiological regulation and roles in inflammation and cancer. , 2012, Physiological reviews.
[36] M. Desai,et al. Intradialytic hypotension and vascular access thrombosis. , 2011, Journal of the American Society of Nephrology : JASN.
[37] R. Reed,et al. Transcapillary exchange: role and importance of the interstitial fluid pressure and the extracellular matrix. , 2010, Cardiovascular research.
[38] M. Weir,et al. Dry-weight: a concept revisited in an effort to avoid medication-directed approaches for blood pressure control in hemodialysis patients. , 2010, Clinical journal of the American Society of Nephrology : CJASN.
[39] A. Friedman,et al. Reassessment of albumin as a nutritional marker in kidney disease. , 2010, Journal of the American Society of Nephrology : JASN.
[40] Yuh-Feng Lin,et al. Applying an Artificial Neural Network to Predict Total Body Water in Hemodialysis Patients , 2005, American Journal of Nephrology.
[41] Rich Caruana,et al. Predicting good probabilities with supervised learning , 2005, ICML.
[42] K. D. Workgroup. K/DOQI clinical practice guidelines for cardiovascular disease in dialysis patients. , 2005, American journal of kidney diseases : the official journal of the National Kidney Foundation.
[43] Bianca Zadrozny,et al. Transforming classifier scores into accurate multiclass probability estimates , 2002, KDD.
[44] W. Hörl,et al. Hemodialysis-associated hypertension: pathophysiology and therapy. , 2002, American journal of kidney diseases : the official journal of the National Kidney Foundation.
[45] L. Breiman. Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.
[46] Bianca Zadrozny,et al. Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers , 2001, ICML.
[47] K. Nitta. 2019 Annual Dialysis Data Report, JSDT Renal Data Registry , 2020 .
[48] I. Masakane. 2016 Annual Dialysis Data Report, JSDT Renal Data Registry , 2018 .
[49] S. Naicker,et al. Correlation between volume overload, chronic inflammation, and left ventricular dysfunction in chronic kidney disease patients. , 2016, Clinical nephrology.
[50] K. Leunissen,et al. Inflammation, overhydration and cardiac biomarkers in haemodialysis patients: a longitudinal study. , 2010, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.
[51] B. Lindholm,et al. Inflammation, malnutrition, and cardiac disease as predictors of mortality in hemodialysis patients. , 2002, Journal of the American Society of Nephrology : JASN.
[52] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..