ArticleBMC medical informatics and decision making2019
A hybrid neural network model for predicting kidney disease in hypertension patients based on electronic health records.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 74 citations in OpenAlex.
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- MAL-Net: A Multi-Label Deep Learning Framework Integrating LSTM and Multi-Head Attention for Enhanced Classification of IgA Nephropathy Subtypes Using Clinical Sensor Data.Sensors (Basel, Switzerland) · 2025Article
- Fine-tuned deep transfer learning: an effective strategy for the accurate chronic kidney disease classification.PeerJ. Computer science · 2025Article
- Application of artificial intelligence in hypertension.Clinical hypertension · 2024Review
- Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning.Sensors (Basel, Switzerland) · 2022Article
- Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease.Healthcare (Basel, Switzerland) · 2022Article
- A Deep Neural Network for Early Detection and Prediction of Chronic Kidney Disease.Diagnostics (Basel, Switzerland) · 2022Article
- Multi-task learning for Chinese clinical named entity recognition with external knowledge.BMC medical informatics and decision making · 2021Article
- Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan.Healthcare (Basel, Switzerland) · 2021Article
- Prediction of blood culture outcome using hybrid neural network model based on electronic health records.BMC medical informatics and decision making · 2020Article
- A comparative study of two methods to predict the incidence of hepatitis B in Guangxi, China.PloS one · 2020Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDisease prediction based on Electronic Health Records (EHR) has become one hot research topic in biomedical community. Existing work mainly focuses on the prediction of one target disease, and little work is proposed for multiple associated diseases prediction. Meanwhile, a piece of EHR usually contains two main information: the textual description and physical indicators. However, existing work largely adopts statistical models with discrete features from numerical physical indicators in EHR, and fails to make full use of textual description information.
methodsIn this paper, we study the problem of kidney disease prediction in hypertension patients by using neural network model. Specifically, we first model the prediction problem as a binary classification task. Then we propose a hybrid neural network which incorporates Bidirectional Long Short-Term Memory (BiLSTM) and Autoencoder networks to fully capture the information in EHR.
resultsWe construct a dataset based on a large number of raw EHR data. The dataset consists of totally 35,332 records from hypertension patients. Experimental results show that the proposed neural model achieves 89.7% accuracy for the task.
conclusionsA hybrid neural network model was presented. Based on the constructed dataset, the comparison results of different models demonstrated the effectiveness of the proposed neural model. The proposed model outperformed traditional statistical models with discrete features and neural baseline systems.
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