Computer Science

Multimodal-based prediction model for acute kidney injury

  • Wei DENG ,
  • Fang ZHOU
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  • School of Data Science and Engineering, East China Normal University, Shanghai 200062, China

Received date: 2022-07-09

  Online published: 2023-07-25

Abstract

Acute kidney injury is a clinical disease with a high morbidity rate, and early identification of potential patients can facilitate medical interventions to reduce morbidity and mortality. In recent years, electronic health records have been widely used to predict an individual’s potential risk. Most of the existing acute kidney injury prediction models tackle the issue of sparsity and irregularity in the physiological variables data by aggregating data or imputing the missing value, but ignore the patient’s health status implied by the missing information. Moreover, they do not consider the characteristics of and correlation between the various modalities. To solve the above issues, we present a multi-modal disease prediction model for acute kidney injury. The proposed model considers a variety of modal data, including physiological variables, disease, and demographic data. A new mask and time span based long short term memory (LSTM) network is designed to learn the time span and missing information of individual Physiological variables, and furthermore, to capture their numerical changes and frequency changes. The multi-head self-attention mechanism is introduced to promote interaction learning of each modality representation. Experiments on the real-world application of acute kidney injury risk prediction and mortality risk prediction demonstrate the effectiveness and rationality of the proposed model.

Cite this article

Wei DENG , Fang ZHOU . Multimodal-based prediction model for acute kidney injury[J]. Journal of East China Normal University(Natural Science), 2023 , 2023(4) : 52 -64 . DOI: 10.3969/j.issn.1000-5641.2023.04.006

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