Prediction of target range of intact parathyroid hormone in hemodialysis patients with artificial neural network

Wang, Yuh-Feng; Hu, Tsung‐Ming; Wu, Chia‐Chao; Yu, Fu-Chiu; Fu, Chao–Ming; Lin, Shih‐Hua; Huang, Wei-Hsin; Chiu, Jainn-Shiun

doi:10.1016/j.cmpb.2006.06.001

Cited by 12 publications

(9 citation statements)

References 25 publications

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“…The average AUROC value observed in the current study compares favourably with previous ANN-based prediction models in medical applications such as predicting psychosis outcomes, predicting response to chemotherapy and classifying tumours (AUROCs 0.70–0.91) [ 13 , 34 , 35 ]. In nephrologic applications, such as screening for glomerulopathy using urine biomarkers, predicting erythropoeitin responsiveness, stratifying PD membrane characteristics and predicting delayed renal allograft dysfunction, AUROC values ranged from 0.65 to 0.95 and sensitivities and specificities ranged from 64 to 92% and 65 to 92%, respectively [ 16–25 ].…”

Section: Discussionmentioning

confidence: 99%

“…ANNs have been used successfully as a prediction tool in a variety of medical and non-medical situations [ 13 ]. In nephrology, ANNs have been used successfully to screen for glomerulopathy using urine biomarkers, to predict erythropoeitin responsiveness, to stratify PD membrane characteristics and to predict delayed renal allograft dysfunction [ 16–25 ].…”

Section: Introductionmentioning

confidence: 99%

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Predicting technique survival in peritoneal dialysis patients: comparing artificial neural networks and logistic regression

Tangri

Ansell

Naimark

2008

Nephrology Dialysis Transplantation

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Background. Early technique failure has been a major limitation on the wider adoption of peritoneal dialysis (PD). The objectives of this study were to use data from a large, multi-centre, prospective database, the United Kingdom Renal Registry (UKRR), in order to determine the ability of an artificial neural network (ANN) model to predict early PD technique failure and to compare its performance with a logistic regression (LR)-based approach.Methods. The analysis included all incident PD patients enrolled in the UKRR from 1999 to 2004. The event of interest was technique failure. For both the ANN and LR analyses a bootstrap approach was used: the data were divided into 20 random training (75%) and validation (25%) sets. Models were derived on the latter and then used to make predictions on the former. Predictive accuracy was assessed by area under the ROC curve (AUROC). The 20 AUROC values and their standard errors were then averaged.Results. There were 3269 patients included in the analysis with a mean age of 59.9 years and a mean observation time of 430 days. Of the patients, 38.3% were female and 90.8% were Caucasian. 1458 patients (44.6%) suffered technique failure. The AUROC for the ANN model was 0.760 ± 0.0167 and the LR model was 0.709 and 0.0208. (P = 0.0164)Conclusions. Using UKRR data, both ANN and LR models predicted early PD technique failure with moderate accuracy. In this study, an ANN outperformed an LR-based approach. As the scope and the completeness of the UKRR increases, the question of whether more sophisticated ANN models will perform even better remains for further study.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting technique survival in peritoneal dialysis patients: comparing artificial neural networks and logistic regression

Tangri

Ansell

Naimark

2008

Nephrology Dialysis Transplantation

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“…Another study describes the use of AI technologies in predicting the consequences of anemia that occurs during hemodialysis [7]. A study using neural networks was also carried out [8], in which the task of predicting the concentration of parathyroid hormone in a patient's blood plasma as a result of calcium-phosphorus metabolism in dialysis patients is solved. Within a study [9.], based on retrospective data (6 months), a personalized dose of erythropoietins is selected as the main treatment against anemia.…”

Section: Introductionmentioning

confidence: 99%

Methodological foundation of comprehensive support for dialysis patients based on artificial intelligence technologies

Лакман¹,

Padukova²,

Nafikov³

2019

Proceedings of the International Scientific and Practical Conference on Digital Economy (ISCDE 2019)

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The existing software products represented by intelligent decision-support systems based on machine learning algorithms have minimal functionality and solve highly targeted problems in the field of hemodialysis. The aim of the study is to formulate the methodological foundations of the development of a medical information system that allows comprehensively addressing the quality of life problems of dialysis patients through the use of machine learning algorithms. A specific practical goal is to develop an intelligent decision support system for prescribing personalized dialysis and drug therapy for patients with chronic renal failure with an assessment of the long-term risks, as well as to assess the effectiveness of the treatment strategy in terms of the effectiveness of the hemodialysis procedure, the validity of prescriptions for restoring calcium and phosphorus metabolism and antianemic therapy based on the patient's profile. A patient's profile is understood as a combination of socio-demographic characteristics of the patient, functional examinations, laboratory and clinical studies of dynamics monitoring, the "history" of pharmacological prescriptions, and also (partially and not for all patients) genetic studies. The machine learning algorithms include: extreme gradient boosting over decision trees, matching, Cox regression (survival analysis). The significance of this study lies in the universality of the proposed methodology for creating an intelligent decision support system for prescribing personalized therapy to patients. The proposed technique can be used to create a similar medical system for other diseases. An important condition for the methodology translation for evaluating the effectiveness of the treatment of other diseases is the collection of data on patients under the constant supervision of a doctor for a long period of time. This applies to patients with diabetes mellitus, cancer patients, patients suffering from cardiovascular diseases.

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“…For instance, AI and ML have addressed problems concerning anemia and other issues in hemodialysis (HD) patients [20,21]. Artificial neural networks were used for predicting total body water in HD patients [22] and the target range of plasma intact parathyroid hormone concentration [23]. Support vector machines and reinforcement learning have been proposed for erythropoietin dosage prediction and personalization [24].…”

Section: Present Artificial Intelligence In Medicine: Challenges For mentioning

confidence: 99%

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

et al. 2018

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Background: Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient’s quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for the assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of “big data” and will require real-time predictive models. 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 of devices that simulate intelligent behavior. The incorporation of artificial intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in artificial intelligence and machine learning, a scientific meeting was organized in the Hospital Universitari Bellvitge (L’Hospitalet, Barcelona). As an outcome of that meeting, the aim of this review is to investigate artificial intel ligence experiences on dialysis, with a focus on potential barriers, challenges, and prospects for future applications of these technologies. Summary and Key Messages: Artificial intelligence research on dialysis is still in an early stage, and the main challenge relies on interpretability and/or comprehensibility of data models when applied to decision making. Artificial neural networks and medical decision support systems have been used to make predictions about anemia, total body water, or intradialysis hypotension and are promising approaches for the prescription and monitoring of hemodialysis therapy. Current dialysis machines are continuously improving due to innovative technological developments, but patient safety is still a key challenge. Real-time monitoring systems, coupled with automatic instantaneous biofeedback, will allow changing dialysis prescriptions continuously. The integration of vital sign monitoring with dialysis parameters will produce large data sets that will require the use of data analysis techniques, possibly from the area of machine learning, in order to make better decisions and increase the safety of patients.

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Prediction of target range of intact parathyroid hormone in hemodialysis patients with artificial neural network

Cited by 12 publications

References 25 publications

Predicting technique survival in peritoneal dialysis patients: comparing artificial neural networks and logistic regression

Predicting technique survival in peritoneal dialysis patients: comparing artificial neural networks and logistic regression

Methodological foundation of comprehensive support for dialysis patients based on artificial intelligence technologies

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

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