Neural Network Modeling to Stratify Peritoneal Membrane Transporter in Predialytic Patients

Chen, Chiou An; Lin, Shih Hua; Hsu, Yi-Chih; Li, Yu-Chuan; Wang, Yuh Feng; Chiu, Jainn Shiun

doi:10.2169/internalmedicine.45.1419

Cited by 8 publications

(5 citation statements)

References 5 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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“…In this study, calibration was assessed using the HosmerLemeshow goodnessoffit (H) statistic, which divides subjects into deciles based on pre dicted probabilities and then computes chisquare values from the observed and expected frequen cies. [19][20][21] A statistically good fitness was defined as P > 0.05. To provide an unbiased estimate of the models' discrimination and calibration, 22 these val ues were calculated from an external data set derived from anesthesia records for the period of March to June 2008 (in total, 343 records were registered for which general anesthesia was the primary anesthetic technique) that were not used in the modelbuilding processing.…”

Section: External Validation Data Setmentioning

confidence: 99%

Application of an Artificial Neural Network to Predict Postinduction Hypotension During General Anesthesia

et al. 2010

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“…Since high peritoneal membrane transport status is associated with higher morbidity and mortality, determining peritoneal membrane transport status can result in a better prognosis. A study used artificial neural network (ANN) model for predialytic stratification of 111 uremic patients on the basis of peritoneal membrane transport status from a 5-year PD database [ 18 ]. The evaluation of peritoneal membrane transport status by the ANN model, if predictable before PD, will help clinicians make decisions about more suitable dialysis modality.…”

Section: Clinical Approachmentioning

confidence: 99%

Artificial intelligence in peritoneal dialysis: general overview

Bai

Tang

2022

Renal Failure

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Objective This article is a general overview about artificial intelligence/machine learning (AI/ML) algorithms in the domain of peritoneal dialysis (PD). Methods We searched studies that used AI/ML in PD, which were classified according to the type of algorithm and PD issue. Results Studies were divided into (a) predialytic stratification, (b) peritoneal technique issues, (c) infections, and (d) complications prediction. Most of the studies were observational and majority of them were reported after 2010. Conclusions There is a number of studies proved that AI/ML algorithms can predict better than conventional statistical method and even nephrologists. However, the soundness of AI/ML algorithms in PD still requires large databases and interpretation by clinical experts. In the future, we hope that AI will facilitate the management of PD patients, thus increasing the quality of life and survival.

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Neural Network Modeling to Stratify Peritoneal Membrane Transporter in Predialytic Patients

Cited by 8 publications

References 5 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

Application of an Artificial Neural Network to Predict Postinduction Hypotension During General Anesthesia

Artificial intelligence in peritoneal dialysis: general overview

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