{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T22:08:25Z","timestamp":1776204505691,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T00:00:00Z","timestamp":1733356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shin-Kong Wu Ho-Su Memorial Hospital","award":["112-SKH-NYCU-06"],"award-info":[{"award-number":["112-SKH-NYCU-06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>This study aims to develop a deep learning model to predict peritoneal dialysis (PD) failure within the next three months using data from the preceding three months. Background: PD patients typically perform treatments at home and visit the clinic only once per month, leading to significant gaps in clinical care and increased risks of PD failure, which may necessitate a transition to hemodialysis (HD). Current studies on PD patients largely focus on predicting PD failure, mortality risk, and hospitalization through traditional machine learning methods, with limited application of deep learning for this purpose. Methods: We collected comprehensive patient data, including demographic information, comorbidities, medication history, biochemical test results, dialysis prescriptions, and peritoneal equilibrium test outcomes. After preprocessing, we employed time-series deep learning models to train and make predictions. Results: The model achieved a prediction accuracy of 89% and an AUROC of 92%, outperforming previous methods used for PD failure prediction. Conclusion: This approach not only improves prediction accuracy but also identifies key features that can aid clinicians in developing more precise treatment plans and enhancing patient care.<\/jats:p>","DOI":"10.3390\/info15120776","type":"journal-article","created":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T06:24:44Z","timestamp":1733379884000},"page":"776","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predicting Peritoneal Dialysis Failure Within the Next Three Months Based on Deep Learning and Important Features Analysis"],"prefix":"10.3390","volume":"15","author":[{"given":"Fang-Yu","family":"Hsu","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence, National Yang Ming Chiao Tung University, Tainan 711, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7996-4184","authenticated-orcid":false,"given":"Ren-Hung","family":"Hwang","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, National Yang Ming Chiao Tung University, Tainan 711, Taiwan"}]},{"given":"Ming-Hsien","family":"Tsai","sequence":"additional","affiliation":[{"name":"Division of Nephrology, Department of Internal Medicine, Shin-Kong Wu Ho-Su Memorial Hospital, Taipei 111, Taiwan"},{"name":"Department of Medicine, Fu-Jen Catholic University School of Medicine, Taipei 242, Taiwan"}]},{"given":"Jing-Tong","family":"Wang","sequence":"additional","affiliation":[{"name":"Division of Nephrology, Department of Internal Medicine, Shin-Kong Wu Ho-Su Memorial Hospital, Taipei 111, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,5]]},"reference":[{"key":"ref_1","first-page":"434608","article-title":"Peritoneal Dialysis Drop-out: Causes and Prevention Strategies","volume":"2011","author":"Lai","year":"2011","journal-title":"Int. J. Nephrol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tsai, M.H., Chen, Y.Y., Jang, T.N., Wang, J.T., and Fang, Y.W. (2022). Outcome Analysis of Transition From Peritoneal Dialysis to Hemodialysis: A Population-Based Study. Front. Med., 9.","DOI":"10.3389\/fmed.2022.876229"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/MCOM.2019.1800155","article-title":"Deep Learning with Long Short-Term Memory for Time Series Prediction","volume":"57","author":"Hua","year":"2019","journal-title":"IEEE Commun. Mag."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mehtab, S., and Sen, J. (2020, January 8\u20139). Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models. Proceedings of the 2020 International Conference on Decision Aid Sciences and Application (DASA), Sakheer, Bahrain.","DOI":"10.1109\/DASA51403.2020.9317207"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhou, Y. (2022, January 18\u201324). Rethinking Reconstruction Autoencoder-Based Out-of-Distribution Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00723"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2972","DOI":"10.1093\/ndt\/gfn187","article-title":"Predicting technique survival in peritoneal dialysis patients: Comparing artificial neural networks and logistic regression","volume":"23","author":"Tangri","year":"2008","journal-title":"Nephrol Dial Transpl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"c93","DOI":"10.1159\/000319988","article-title":"Determining factors that predict technique survival on peritoneal dialysis: Application of regression and artificial neural network methods","volume":"118","author":"Tangri","year":"2011","journal-title":"Nephron Clin. Pract."},{"key":"ref_8","first-page":"602","article-title":"Application of recurrent neural network in prognosis of peritoneal dialysis","volume":"51","author":"Tang","year":"2019","journal-title":"Beijing Da Xue Xue Bao Yi Xue Ban"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Noh, J., Yoo, K.D., Bae, W., Lee, J.S., Kim, K., Cho, J.H., Lee, H., Kim, D.K., Lim, C.S., and Kang, S.W. (2020). Prediction of the mortality risk in peritoneal dialysis patients using machine learning models: A nation-wide prospective cohort in Korea. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-64184-0"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.21037\/atm-20-1006","article-title":"Development of a scoring tool for predicting prolonged length of hospital stay in peritoneal dialysis patients through data mining","volume":"8","author":"Wu","year":"2020","journal-title":"Ann. Transl. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e17886","DOI":"10.2196\/17886","article-title":"Predicting prolonged length of hospital stay for peritoneal dialysis-treated patients using stacked generalization: Model development and validation study","volume":"9","author":"Kong","year":"2021","journal-title":"JMIR Med. Inform."},{"key":"ref_12","first-page":"335","article-title":"Selection of peritoneal dialysis schemes based on multi-objective fuzzy pattern recognition","volume":"22","author":"Zhang","year":"2005","journal-title":"Sheng Wu Yi Xue Gong Cheng Xue Za Zhi"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2169\/internalmedicine.45.1419","article-title":"Neural network modeling to stratify peritoneal membrane transporter in predialytic patients","volume":"45","author":"Chen","year":"2006","journal-title":"Intern. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.kint.2017.01.017","article-title":"Machine-learning algorithms define pathogen-specific local immune fingerprints in peritoneal dialysis patients with bacterial infections","volume":"92","author":"Zhang","year":"2017","journal-title":"Kidney Int."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1016\/j.procs.2017.08.296","article-title":"Understanding Stroke in Dialysis and Chronic Kidney Disease","volume":"113","author":"Rodrigues","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1080\/0886022X.2022.2064304","article-title":"Artificial intelligence in peritoneal dialysis: General overview","volume":"44","author":"Bai","year":"2022","journal-title":"Ren. Fail."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1007\/s11276-018-01905-4","article-title":"A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis","volume":"28","author":"Brito","year":"2022","journal-title":"Wirel. Netw."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Petmezas, G., Papageorgiou, V.E., Vassilikos, V., Pagourelias, E., Tsaklidis, G., Katsaggelos, A.K., and Maglaveras, N. (2024). Recent Advancements and Applications of Deep Learning in Heart Failure: A Systematic Review. Comput. Biol. Med., 176.","DOI":"10.1016\/j.compbiomed.2024.108557"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107956","DOI":"10.1016\/j.compchemeng.2022.107956","article-title":"A Tutorial Review of Neural Network Modeling Approaches for Model Predictive Control","volume":"107","author":"Ren","year":"2022","journal-title":"Comput. Chem. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Pessoa, D., Petmezas, G., Papageorgiou, V.E., Rocha, B.M., Stefanopoulos, L., Kilintzis, V., Maglaveras, N., Frerichs, I., de Carvalho, P., and Paiva, R.P. (2023, January 19\u201321). Pediatric Respiratory Sound Classification Using a Dual Input Deep Learning Architecture. Proceedings of the 2023 IEEE Biomedical Circuits and Systems Conference (BioCAS), Toronto, ON, Canada.","DOI":"10.1109\/BioCAS58349.2023.10388733"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","article-title":"Finding Structure in Time","volume":"14","author":"Elman","year":"1990","journal-title":"Cogn. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning Internal Representations by Error Propagation","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_24","unstructured":"Lundberg, S.M., and Lee, S.-I. (2017, January 4\u20139). A Unified Approach to Interpreting Model Predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS\u201917), Long Beach, CA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Papageorgiou, V.E., Zegkos, T., Efthimiadis, G., and Tsaklidis, G. (2022). Analysis of Digitalized ECG Signals Based on Artificial Intelligence and Spectral Analysis Methods Specialized in ARVC. Int. J. Numer. Methods Biomed. Eng., 38.","DOI":"10.1002\/cnm.3644"},{"key":"ref_26","first-page":"587","article-title":"Brain Tumor Detection Based on Features Extracted and Classified Using a Low-Complexity Neural Network","volume":"38","author":"Papageorgiou","year":"2021","journal-title":"Trait. Signal"},{"key":"ref_27","unstructured":"Loshchilov, I., and Hutter, F. (2019, January 6\u20139). Decoupled Weight Decay Regularization. Proceedings of the 7th International Conference on Learning Representations (ICLR), New Orleans, LA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Papageorgiou, V.E., Dogoulis, P., and Papageorgiou, D.P. (2023, January 20\u201323). A Convolutional Neural Network of Low Complexity for Tumor Anomaly Detection. Proceedings of the Eighth International Congress on Information and Communication Technology, ICICT 2023, London, UK.","DOI":"10.1007\/978-981-99-3236-8_78"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/12\/776\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:47:08Z","timestamp":1760114828000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/12\/776"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,5]]},"references-count":28,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["info15120776"],"URL":"https:\/\/doi.org\/10.3390\/info15120776","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,5]]}}}