Time-aware Context-Gated Graph Attention Network for Clinical Risk Prediction

Xu, Yuyang; Ying, Haochao; Qian, Siyi; Zhuang, Fuzhen; Zhang, Xiao; Wang, Deqing; Wu, Jian; Xiong, Hui

doi:10.1109/tkde.2022.3181780

Cited by 11 publications

(2 citation statements)

References 32 publications

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“…Recently, deep learning techniques have shown excellent performance in disease risk prediction (Xu et al 2022), medication recommendation (An et al 2021), and diagnosis prediction (Chen et al 2018). We focus on diagnosis prediction studies from two following perspectives.…”

Section: Related Workmentioning

confidence: 99%

KerPrint: Local-Global Knowledge Graph Enhanced Diagnosis Prediction for Retrospective and Prospective Interpretations

Yang¹,

Zou

et al. 2023

AAAI

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While recent developments of deep learning models have led to record-breaking achievements in many areas, the lack of sufficient interpretation remains a problem for many specific applications, such as the diagnosis prediction task in healthcare. The previous knowledge graph(KG) enhanced approaches mainly focus on learning clinically meaningful representations, the importance of medical concepts, and even the knowledge paths from inputs to labels. However, it is infeasible to interpret the diagnosis prediction, which needs to consider different medical concepts, various medical relationships, and the time-effectiveness of knowledge triples in different patient contexts. More importantly, the retrospective and prospective interpretations of disease processes are valuable to clinicians for the patients' confounding diseases. We propose KerPrint, a novel KG enhanced approach for retrospective and prospective interpretations to tackle these problems. Specifically, we propose a time-aware KG attention method to solve the problem of knowledge decay over time for trustworthy retrospective interpretation. We also propose a novel element-wise attention method to select candidate global knowledge using comprehensive representations from the local KG for prospective interpretation. We validate the effectiveness of our KerPrint through an extensive experimental study on a real-world dataset and a public dataset. The results show that our proposed approach not only achieves significant improvement over knowledge-enhanced methods but also gives the interpretability of diagnosis prediction in both retrospective and prospective views.

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Section: Related Workmentioning

confidence: 99%

KerPrint: Local-Global Knowledge Graph Enhanced Diagnosis Prediction for Retrospective and Prospective Interpretations

Yang¹,

Zou

et al. 2023

AAAI

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show abstract

“…With the growing demand of long-term time series forecasting accuracy in various domains [8], [58], [60], [61], [62], [63], [64], [65], [66], traditional forecasting models, e.g. ARIMA [20], [21], SES [67], are no longer able to deal with more and more complicated forecasting situations.…”

Section: Appendix B Related Workmentioning

confidence: 99%

Respecting Time Series Properties Makes Deep Time Series Forecasting Perfect

Shen¹,

Wei²,

Wang³

2022

Preprint

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How to handle time features shall be the core question of any time series forecasting model. Ironically, it is often ignored or misunderstood by deep-learning based models, even those baselines which are state-of-the-art. This behavior makes their inefficient, untenable and unstable. In this paper, we rigorously analyze three prevalent but deficient/unfounded deep time series forecasting mechanisms or methods from the view of time series properties, including normalization methods, multivariate forecasting and input sequence length. Corresponding corollaries and solutions are given on both empirical and theoretical basis. We thereby propose a novel time series forecasting network, i.e. RTNet, on the basis of aforementioned analysis. It is general enough to be combined with both supervised and self-supervised forecasting format. Thanks to the core idea of respecting time series properties, no matter in which forecasting format, RTNet shows obviously superior forecasting performances compared with dozens of other SOTA time series forecasting baselines in three real-world benchmark datasets. By and large, it even occupies less time complexity and memory usage while acquiring better forecasting accuracy. The source code is available at https://github.com/OrigamiSL/RTNet.

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TransLSTD: Augmenting hierarchical disease risk prediction model with time and context awareness via disease clustering

You,

Dang,

et al. 2024

Information Systems

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Time-aware Context-Gated Graph Attention Network for Clinical Risk Prediction

Cited by 11 publications

References 32 publications

KerPrint: Local-Global Knowledge Graph Enhanced Diagnosis Prediction for Retrospective and Prospective Interpretations

KerPrint: Local-Global Knowledge Graph Enhanced Diagnosis Prediction for Retrospective and Prospective Interpretations

Respecting Time Series Properties Makes Deep Time Series Forecasting Perfect

TransLSTD: Augmenting hierarchical disease risk prediction model with time and context awareness via disease clustering

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