Prediction of clinical events in hemodialysis patients using an artificial neural network

Firdani Rianda Putra, Aldilas Achmad Nursetyo, Saurabh Singh Thakur, Ram Babu Roy, Shabbir Syed-Abdul, Shwetambara Malwade, Yu Chuan Lia

研究成果: 書貢獻/報告類型會議貢獻

摘要

Advanced chronic kidney disease (CKD) requires routine renal replacement therapy (RRT) that involves hemodialysis (HD) which may cause increased risk of muscle spasms, cardiovascular events, and death. We used Artificial Neural Network (ANN) method to predict clinical events during the HD sessions. The vital signs, captured using a non-contact bed-sensor, and demographic information from the electronic medical records for 109 patients enrolled in the study was used. Weka Workbench software was used to train and validate the ANN model. The prediction model was built using a Multilayer perceptron (MLP) algorithm as part of the ANN with 10-fold cross-validation. The model showed mean precision and recall of 93.45% and AUC of 96.7%. Age was the most important variable for static feature and heart rate for dynamic feature. This model can be used to predict the risk of clinical events among HD patients and can support decision-making for healthcare professionals.

原文英語
主出版物標題MEDINFO 2019
主出版物子標題Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
編輯Brigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi
發行者IOS Press
頁面1570-1571
頁數2
ISBN(電子)9781643680026
DOIs
出版狀態已發佈 - 八月 21 2019
事件17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, 法国
持續時間: 八月 25 2019八月 30 2019

出版系列

名字Studies in Health Technology and Informatics
264
ISSN(列印)0926-9630
ISSN(電子)1879-8365

會議

會議17th World Congress on Medical and Health Informatics, MEDINFO 2019
國家法国
城市Lyon
期間8/25/198/30/19

指紋

Renal Dialysis
Neural Networks (Computer)
Neural networks
Renal Replacement Therapy
Vital Signs
Electronic Health Records
Spasm
Chronic Renal Insufficiency
Electronic medical equipment
Area Under Curve
Decision Making
Multilayer neural networks
Software
Heart Rate
Demography
Delivery of Health Care
Muscle
Decision making
Sensors

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

引用此文

Putra, F. R., Nursetyo, A. A., Thakur, S. S., Roy, R. B., Syed-Abdul, S., Malwade, S., & Lia, Y. C. (2019). Prediction of clinical events in hemodialysis patients using an artificial neural network. 於 B. Seroussi, L. Ohno-Machado, L. Ohno-Machado, & B. Seroussi (編輯), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics (頁 1570-1571). (Studies in Health Technology and Informatics; 卷 264). IOS Press. https://doi.org/10.3233/SHTI190539

Prediction of clinical events in hemodialysis patients using an artificial neural network. / Putra, Firdani Rianda; Nursetyo, Aldilas Achmad; Thakur, Saurabh Singh; Roy, Ram Babu; Syed-Abdul, Shabbir; Malwade, Shwetambara; Lia, Yu Chuan.

MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. 編輯 / Brigitte Seroussi; Lucila Ohno-Machado; Lucila Ohno-Machado; Brigitte Seroussi. IOS Press, 2019. p. 1570-1571 (Studies in Health Technology and Informatics; 卷 264).

研究成果: 書貢獻/報告類型會議貢獻

Putra, FR, Nursetyo, AA, Thakur, SS, Roy, RB, Syed-Abdul, S, Malwade, S & Lia, YC 2019, Prediction of clinical events in hemodialysis patients using an artificial neural network. 於 B Seroussi, L Ohno-Machado, L Ohno-Machado & B Seroussi (編輯), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. Studies in Health Technology and Informatics, 卷 264, IOS Press, 頁 1570-1571, 17th World Congress on Medical and Health Informatics, MEDINFO 2019, Lyon, 法国, 8/25/19. https://doi.org/10.3233/SHTI190539
Putra FR, Nursetyo AA, Thakur SS, Roy RB, Syed-Abdul S, Malwade S 等. Prediction of clinical events in hemodialysis patients using an artificial neural network. 於 Seroussi B, Ohno-Machado L, Ohno-Machado L, Seroussi B, 編輯, MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. IOS Press. 2019. p. 1570-1571. (Studies in Health Technology and Informatics). https://doi.org/10.3233/SHTI190539
Putra, Firdani Rianda ; Nursetyo, Aldilas Achmad ; Thakur, Saurabh Singh ; Roy, Ram Babu ; Syed-Abdul, Shabbir ; Malwade, Shwetambara ; Lia, Yu Chuan. / Prediction of clinical events in hemodialysis patients using an artificial neural network. MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. 編輯 / Brigitte Seroussi ; Lucila Ohno-Machado ; Lucila Ohno-Machado ; Brigitte Seroussi. IOS Press, 2019. 頁 1570-1571 (Studies in Health Technology and Informatics).
@inproceedings{5c3e7c32b7da4aa385066068984057cf,
title = "Prediction of clinical events in hemodialysis patients using an artificial neural network",
abstract = "Advanced chronic kidney disease (CKD) requires routine renal replacement therapy (RRT) that involves hemodialysis (HD) which may cause increased risk of muscle spasms, cardiovascular events, and death. We used Artificial Neural Network (ANN) method to predict clinical events during the HD sessions. The vital signs, captured using a non-contact bed-sensor, and demographic information from the electronic medical records for 109 patients enrolled in the study was used. Weka Workbench software was used to train and validate the ANN model. The prediction model was built using a Multilayer perceptron (MLP) algorithm as part of the ANN with 10-fold cross-validation. The model showed mean precision and recall of 93.45{\%} and AUC of 96.7{\%}. Age was the most important variable for static feature and heart rate for dynamic feature. This model can be used to predict the risk of clinical events among HD patients and can support decision-making for healthcare professionals.",
keywords = "Electronic health records, Neural networks, Renal dialysis",
author = "Putra, {Firdani Rianda} and Nursetyo, {Aldilas Achmad} and Thakur, {Saurabh Singh} and Roy, {Ram Babu} and Shabbir Syed-Abdul and Shwetambara Malwade and Lia, {Yu Chuan}",
year = "2019",
month = "8",
day = "21",
doi = "10.3233/SHTI190539",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "1570--1571",
editor = "Brigitte Seroussi and Lucila Ohno-Machado and Lucila Ohno-Machado and Brigitte Seroussi",
booktitle = "MEDINFO 2019",
address = "Netherlands",

}

TY - GEN

T1 - Prediction of clinical events in hemodialysis patients using an artificial neural network

AU - Putra, Firdani Rianda

AU - Nursetyo, Aldilas Achmad

AU - Thakur, Saurabh Singh

AU - Roy, Ram Babu

AU - Syed-Abdul, Shabbir

AU - Malwade, Shwetambara

AU - Lia, Yu Chuan

PY - 2019/8/21

Y1 - 2019/8/21

N2 - Advanced chronic kidney disease (CKD) requires routine renal replacement therapy (RRT) that involves hemodialysis (HD) which may cause increased risk of muscle spasms, cardiovascular events, and death. We used Artificial Neural Network (ANN) method to predict clinical events during the HD sessions. The vital signs, captured using a non-contact bed-sensor, and demographic information from the electronic medical records for 109 patients enrolled in the study was used. Weka Workbench software was used to train and validate the ANN model. The prediction model was built using a Multilayer perceptron (MLP) algorithm as part of the ANN with 10-fold cross-validation. The model showed mean precision and recall of 93.45% and AUC of 96.7%. Age was the most important variable for static feature and heart rate for dynamic feature. This model can be used to predict the risk of clinical events among HD patients and can support decision-making for healthcare professionals.

AB - Advanced chronic kidney disease (CKD) requires routine renal replacement therapy (RRT) that involves hemodialysis (HD) which may cause increased risk of muscle spasms, cardiovascular events, and death. We used Artificial Neural Network (ANN) method to predict clinical events during the HD sessions. The vital signs, captured using a non-contact bed-sensor, and demographic information from the electronic medical records for 109 patients enrolled in the study was used. Weka Workbench software was used to train and validate the ANN model. The prediction model was built using a Multilayer perceptron (MLP) algorithm as part of the ANN with 10-fold cross-validation. The model showed mean precision and recall of 93.45% and AUC of 96.7%. Age was the most important variable for static feature and heart rate for dynamic feature. This model can be used to predict the risk of clinical events among HD patients and can support decision-making for healthcare professionals.

KW - Electronic health records

KW - Neural networks

KW - Renal dialysis

UR - http://www.scopus.com/inward/record.url?scp=85071420486&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85071420486&partnerID=8YFLogxK

U2 - 10.3233/SHTI190539

DO - 10.3233/SHTI190539

M3 - Conference contribution

C2 - 31438236

AN - SCOPUS:85071420486

T3 - Studies in Health Technology and Informatics

SP - 1570

EP - 1571

BT - MEDINFO 2019

A2 - Seroussi, Brigitte

A2 - Ohno-Machado, Lucila

A2 - Ohno-Machado, Lucila

A2 - Seroussi, Brigitte

PB - IOS Press

ER -