A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction

Liu, Sicen; Li, Tao; Ding, Haoyang; Tang, Buzhou; Wang, Xiaolong; Chen, Qingcai; Yan, Jun; Zhou, Yi

doi:10.1007/s13042-020-01155-x

Cited by 38 publications

(18 citation statements)

References 29 publications

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“…For further improving the classification efficiency, multiple effective ways [ 27 , 28 ] have been developed from several perspectives. In more recent studies, one of the most promising strategies is dynamic selection [ 29 ], in which the most competent or an ensemble classifier is selected by estimating each classifier’s competence level in the classification pool. The benefit of this approach is to identify different unknown samples by choosing different optimum classifiers.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Zhao

Wang

et al. 2021

Entropy

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Imbalance ensemble classification is one of the most essential and practical strategies for improving decision performance in data analysis. There is a growing body of literature about ensemble techniques for imbalance learning in recent years, the various extensions of imbalanced classification methods were established from different points of view. The present study is initiated in an attempt to review the state-of-the-art ensemble classification algorithms for dealing with imbalanced datasets, offering a comprehensive analysis for incorporating the dynamic selection of base classifiers in classification. By conducting 14 existing ensemble algorithms incorporating a dynamic selection on 56 datasets, the experimental results reveal that the classical algorithm with a dynamic selection strategy deliver a practical way to improve the classification performance for both a binary class and multi-class imbalanced datasets. In addition, by combining patch learning with a dynamic selection ensemble classification, a patch-ensemble classification method is designed, which utilizes the misclassified samples to train patch classifiers for increasing the diversity of base classifiers. The experiments’ results indicate that the designed method has a certain potential for the performance of multi-class imbalanced classification.

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Section: Introductionmentioning

confidence: 99%

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Zhao

Wang

et al. 2021

Entropy

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“…GCNs have been utilised in many fields, including computer vision applications, person re-identification, action localisation and also in medical image analysis. These networks were successfully used, e.g., for diagnosis prediction, prescription prediction and biomarker identification [ 135 , 136 , 137 ]. Zhang et al [ 128 ] considered the supervoxels from the brain MRI volume as the nodes of the graph and used GCN to classify supervoxels into different types of tissues.…”

Section: Usage Of Neural Network For Medical Data Analysismentioning

confidence: 99%

Applications of Neural Networks in Biomedical Data Analysis

et al. 2022

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Neural networks for deep-learning applications, also called artificial neural networks, are important tools in science and industry. While their widespread use was limited because of inadequate hardware in the past, their popularity increased dramatically starting in the early 2000s when it became possible to train increasingly large and complex networks. Today, deep learning is widely used in biomedicine from image analysis to diagnostics. This also includes special topics, such as forensics. In this review, we discuss the latest networks and how they work, with a focus on the analysis of biomedical data, particularly biomarkers in bioimage data. We provide a summary on numerous technical aspects, such as activation functions and frameworks. We also present a data analysis of publications about neural networks to provide a quantitative insight into the use of network types and the number of journals per year to determine the usage in different scientific fields.

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“…al in [8] for learning graph structures using EHR data which is used for Alzheimer's disease prediction, mortality and readmission prediction. In [9], RGNN, a combination of recurrent neural networks and GNNs, is used for next-period prescription prediction. Graph Convolutional Transformer (GCT) [10] is used with EHR data for graph construction and readmission prediction.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, GNNs are considered to be more effective to capture not only the values of the features but also their relationships with each other including the sequential or temporal relationships. This is the driver behind the surge in using graph networks in order to accomplish different prediction tasks [6][7][8][9][10] in the application areas as diverse as recommendation systems [7], protein classification [11] and diagnosis prediction [6].…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Patient Graph Embedding in Readmission Prediction

Daneshvar¹,

Samavi²

2022

Proceedings of the Canadian Conference on Artificial Intelligence

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The number of youth seeking mental health services has been increasing in the past decade. Accurate prediction of hospital readmission is a contributing factor in addressing youth mental health problem and healthcare service utilization. Medical records are an important source of information for readmission prediction, however utilizing such records in the context of mental care, requires overcoming two impeding challenges: the diversity of service utilization (e.g., using psychiatric vs. overdose vs. trauma clinics all for the same underlying mental health reason) and the heterogeneity of data associated with each service. Graph Neural Network (GNN) is shown to be promising in performing classification or regression tasks when input data bears a complex structure. In this research, we propose using graph embedding to first generate patient graph that captures episodic emergency department visits and the complex service utilizations of the patient. We then use GNN for readmission prediction. For embedding and training purposes, we utilize more than 4,000 unique mental health patients data over 19 years. To evaluate our approach we systematically compare a variety of of GNN models with four RNN models, namely LSTM, Bi-LSTM, GRU, and Bi-GRU. Our experimental evaluation demonstrates that encoding the complex interrelationship between features of a patient using graph embedding and GNN improves the performance of the predictive model compared to RNN counterparts.

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A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction

Cited by 38 publications

References 29 publications

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Applications of Neural Networks in Biomedical Data Analysis

Heterogeneous Patient Graph Embedding in Readmission Prediction

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