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Predicting Quality of Life Changes in Hemodialysis Patients Using Machine Learning: Generation of an Early Warning System

Shoab Saadat, Ayesha Aziz, Hira Ahmad, Hira Imtiaz, Zara S Sohail, Alvina Kazmi, Sanaa Aslam, Naveen Naqvi, Sidra Saadat
2017 Cureus  
to produce an early warning system, dialysis data interpretation for algorithmic-prediction on quality of life (DIAL), using machine learning to predict a change in QOL in a hemodialysis patient over  ...  Since there has been no study specifically aimed at the most important predictors of QOL in order of their strength of association using modern machine learning techniques, the purpose of this study is  ...  The use of machine learning techniques in the health sector will help doctors make smart decisions.  ... 
doi:10.7759/cureus.1713 pmid:29188157 pmcid:PMC5703595 fatcat:vm7awepdx5f6jhcybunm7wlaym

A novel approach to dry weight adjustments for dialysis patients using machine learning

Hae Ri Kim, Hong Jin Bae, Jae Wan Jeon, Young Rok Ham, Ki Ryang Na, Kang Wook Lee, Yun Kyong Hyon, Dae Eun Choi
2021 PLoS ONE  
In this study, we tried to predict the clinically proper dry weight (DWCP) using machine learning for patient's clinical information including BIS.  ...  Knowledge of the proper dry weight plays a critical role in the efficiency of dialysis and the survival of hemodialysis patients.  ...  In the future, if more patients are included to increase the prediction accuracy using machine learning, this technique will be helpful in establishing the appropriate DW for patients.  ... 
doi:10.1371/journal.pone.0250467 pmid:33891656 pmcid:PMC8064601 fatcat:tqgwqfvu7fczflty5ii5ijvf6m

Machine learning approach for prediction of hematic parameters in hemodialysis patients

Cristoforo Decaro, Giovanni Battista Montanari, Riccardo Molinariz, Alessio Gilberti, Davide Bagnoli, Marco Bianconix, Gaetano Bellanca
2019 IEEE Journal of Translational Engineering in Health and Medicine  
Objective: This paper shows the application of machine learning techniques to predict hematic parameters using blood visible spectra during ex-vivo treatments.  ...  Results & Conclusion: Results of different machine learning algorithms are compared, showing that support vector machine is the best technique for the prediction of hematocrit and oxygen saturation.  ...  The setup here proposed, consisting in the use of mini spectrometer and machine learning techniques, allows results which are comparable with other non-invasive sensors [29] , [30] for prediction of  ... 
doi:10.1109/jtehm.2019.2938951 pmid:32309060 pmcid:PMC6788674 fatcat:5cpax57ekfbd3hx5tkwhmaq5qe

Predicting anemia using NIR spectrum of spent dialysis fluid in hemodialysis patients

Valentina Matović, Branislava Jeftić, Jasna Trbojević-Stanković, Lidija Matija
2021 Scientific Reports  
NIR spectroscopy and machine learning were used as a method to detect anemia in hemodialysis patients.  ...  between the spectrum of spent dialysate in the wavelength range of 700–1700 nm and the levels of hemoglobin (Hb), red blood cells (RBC), hematocrit (Hct), iron (Fe), total iron binding capacity (TIBC)  ...  Acknowledgements Funding This research has been funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia, through Project III 41006.  ... 
doi:10.1038/s41598-021-88821-4 pmid:34006867 fatcat:on7iezyt2zce7pgsmqgdgfyzba

Hierarchical clustering analysis for predicting one-year mortality after starting hemodialysis

Yohei Komaru, Teruhiko Yoshida, Yoshifumi Hamasaki, Masaomi Nangaku, Kent Doi
2020 Kidney International Reports  
Patients in cluster 3 showed lower systolic blood pressures, and lower serum creatinine and urinary liver-type fatty acid-binding protein levels, before the initiation of hemodialysis.  ...  We aimed to conduct a pilot study for better risk stratification, applying machine learning-based classification to patients with ESRD who newly started maintenance hemodialysis.  ...  We hypothesized that a machine learning-derived technique would be useful for the risk stratification of patients on maintenance hemodialysis.  ... 
doi:10.1016/j.ekir.2020.05.007 pmid:32775818 pmcid:PMC7403509 fatcat:276bxmu67jdefbim4pamciimeq

Performance of a Predictive Model for Long-Term Hemoglobin Response to Darbepoetin and Iron Administration in a Large Cohort of Hemodialysis Patients

Carlo Barbieri, Elena Bolzoni, Flavio Mari, Isabella Cattinelli, Francesco Bellocchio, José D. Martin, Claudia Amato, Andrea Stopper, Emanuele Gatti, Iain C. Macdougall, Stefano Stuard, Bernard Canaud (+1 others)
2016 PLoS ONE  
Our model was able to predict individual variation of hemoglobin concentration 3 months in the future with a Mean Absolute Error (MAE) of 0.75 g/dL.  ...  Our ANN predictive model offers a simple and reliable tool applicable in daily clinical practice for predicting the long-term response to ESA/iron therapy of HD patients.  ...  Predictive modeling was performed combining well-established machine learning (ML) techniques with careful feature engineering guided by the principles of actual drug kinetics to long-acting ESA therapy  ... 
doi:10.1371/journal.pone.0148938 pmid:26939055 pmcid:PMC4777424 fatcat:ita6dlil65dyllybvuv6csv6ry

Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy

Miguel Hueso, Alfredo Vellido, Nuria Montero, Carlo Barbieri, Rosa Ramos, Manuel Angoso, Josep Maria Cruzado, Anders Jonsson
2018 Kidney Diseases  
These may come from the fields of machine learning and computational intelligence, both included in artificial intelligence, a branch of engineering involved with the creation  ...  The analysis and evaluation of the resulting large-scale data sets enters the realm of "big data" and will require real-time predictive models.  ...  Acknowledgment We thank Estanislao Navarro for the critical reading of the manuscript.  ... 
doi:10.1159/000486394 pmid:29594137 pmcid:PMC5848485 fatcat:x24qgxt2bnh7xnzwafc4o2vnvy

Role of Artificial Intelligence in Kidney Disease

Qiongjing Yuan, Haixia Zhang, Tianci Deng, Shumei Tang, Xiangning Yuan, Wenbin Tang, Yanyun Xie, Huipeng Ge, Xiufen Wang, Qiaoling Zhou, Xiangcheng Xiao
2020 International Journal of Medical Sciences  
Owing to the huge number of patients, kidney disease remains a global health problem. Challenges remain in its diagnosis and treatment.  ...  Although the number of studies related to AI applications in kidney disease is small, the potential of AI in the management of kidney disease is well recognized by clinicians; AI will greatly enhance clinicians  ...  [18] built risk prediction models for AKI in 157 severely burned patients, and compare the prediction performance of XGBoost machine learning and logistic regression model; machine learning method was  ... 
doi:10.7150/ijms.42078 pmid:32308551 pmcid:PMC7163364 fatcat:bh5zsn5sbjdgxnrejxb53p6wui

Prediction of Serum Creatinine in Hemodialysis Patients Using a Kernel Approach for Longitudinal Data

Mohammad Moqaddasi Amiri, Leili Tapak, Javad Faradmal, Javad Hosseini, Ghodratollah Roshanaei
2020 Healthcare Informatics Research  
We used a longitudinal dataset of hemodialysis patients in Hamadan city between 2013 and 2016.  ...  To evaluate the performance of the methods in serum creatinine prediction, the data was divided into two sets of training and testing samples. Then LR, LMM, LS-SVR, and MLS-SVR were fitted.  ...  Acknowledgments This study was a part of PhD thesis of the first author in Biostatistics and funded by the Vice-Chancellor for Research and Technology of Hamadan University of Medical Sciences (No. 9609286041  ... 
doi:10.4258/hir.2020.26.2.112 pmid:32547808 pmcid:PMC7278511 fatcat:y4nyziolbjgannno23el4ukeq4

Predicting the Effect of Parathyroidectomy on Patient Survival in Secondary Hyperparathyroidism with Machine Learning

Oktoria Oktoria, Cheng-Hong Yang, Jin-Bor Chen
2017 International Journal of Pharma Medicine and Biological Sciences  
In this study, the writer hypothesized that Machine Learning (ML) could predict the effect of PTX based on readily available clinical and laboratory indicators.  ...  There were 158 consecutive HD patients who underwent PTX before 2009 and 275 consecutive hemodialysis (HD) patients without PTX as controls from those visiting the Kaohsiung Chang Gung Memorial Hospital  ...  ACKNOWLEDGMENT I would like to express my gratitude to all those who gave me the possibility to complete this paper. The Special thank goes to DIKTI and State University of Padang, Indonesia.  ... 
doi:10.18178/ijpmbs.6.2.58-62 fatcat:2od56lghyrgydkzolnp6m6n5hi

Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review

Alexandru Burlacu, Adrian Iftene, Daniel Jugrin, Iolanda Valentina Popa, Paula Madalina Lupu, Cristiana Vlad, Adrian Covic
2020 BioMed Research International  
The purpose of this review is to depict current research and impact of artificial intelligence/machine learning (AI/ML) algorithms on dialysis and kidney transplantation.  ...  In the coming years, one would probably witness the emergence of AI/ML devices that facilitate the management of dialysis patients, thus increasing the quality of life and survival.  ...  Supplemental Table 2 : short description and results of the studies involving AI in PD.  ... 
doi:10.1155/2020/9867872 pmid:32596403 pmcid:PMC7303737 fatcat:xtqwjydqnzaezicg56zrwekl4q

Artificial Intelligence in Nephrology: How Can Artificial Intelligence Augment Nephrologists' Intelligence?

Guotong Xie, Tiange Chen, Yingxue Li, Tingyu Chen, Xiang Li, Zhihong Liu
2019 Kidney Diseases  
Artificial intelligence (AI) now plays a critical role in almost every area of our daily lives and academic disciplines due to the growth of computing power, advances in methods and techniques, and the  ...  need to be collected and prepared; a consensus on ethics and safety in the use of AI technologies needs to be built.  ...  The authors have no conflicts of interest to declare. Funding Sources The authors did not receive any funding. Author Contributions G.X., Z.L., and X.L. conceived and designed the study.  ... 
doi:10.1159/000504600 pmid:32021868 pmcid:PMC6995978 fatcat:hm3lwrzjjffifkd26uesh6oh44

Intelligent Prediction Techniques for Chronic Kidney Disease Data Analysis

Shanmugarajeshwari V., Ilayaraja M.
2021 International Journal of Artificial Intelligence and Machine Learning  
This work aims to design a machine-based diagnostic approach using various techniques.  ...  patients with risk level by analyzing the chronic kidney disease dataset.  ...  learning techniques in healthcare has become a real-world emerging for obtaining accurate results of medical diagnosis, using the machine learning techniques involved the healthcare is evolving into a  ... 
doi:10.4018/ijaiml.20210701.oa2 fatcat:zoz2f4gjgjcdbgaa76q57ohupq

Predicting Adverse Outcomes in End Stage Renal Disease: Machine Learning Applied to the United States Renal Data System

Zeid Khitan, Alexis D. Jacob, Courtney Balentine, Adam N. Jacob, Juan R. Sanabria, Joseph I. Shapiro
2018 Marshall Journal of Medicine  
Predicting adverse outcomes in chronic kidney disease using machine learning methods: data from the modification of diet in renal disease.  ...  Support vector machines to model presence/absence of Alburnus alburnus alborella (Teleostea, Cyprinidae) in North-Western Italy: comparison with other machine learning techniques.  ...  apply(, 2, all)] # automatically get rid of empty cols at the end #set up outcome variable as "yes" or "no" for subsequent machine learning A=NULL mm=dim(dat1) [ nnet.ROC=roc(response = testset  ... 
doi:10.18590/mjm.2018.vol4.iss4.8 fatcat:osx5vx76enefjdu7clppeev3pi

Predictive modeling for improved anemia management in dialysis patients

Michael E. Brier, Adam E. Gaweda
2011 Current opinion in nephrology and hypertension  
This work showed that hemoglobin variability can be decreased using predictive models of hemoglobin response.  ...  Summary-Predictive models of hemoglobin response improve anemia management by decreasing hemoglobin variability. This will result in more patients within the target range.  ...  the Current World Literature section in this issue (p. 676).  ... 
doi:10.1097/mnh.0b013e32834bba4e pmid:21941178 pmcid:PMC3758116 fatcat:o4cewriiqba53pqsej3k5mjaxm
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