Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health Records

Shuai NIU, Qing YIN, Yunya SONG, Yi-Ke GUO, Xian YANG*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

3 Citations (Scopus)


Disease risk prediction has attracted increasing attention in the field of modern healthcare, especially with the latest advances in artificial intelligence (AI). Electronic health records (EHRs), which contain heterogeneous patient information, are widely used in disease risk prediction tasks. One challenge of applying AI models for risk prediction lies in generating interpretable evidence to support the prediction results while retaining the prediction ability. In order to address this problem, we propose the method of jointly embedding words and labels whereby attention modules learn the weights of words from medical notes according to their relevance to the names of risk prediction labels. This approach boosts interpretability by employing an attention mechanism and including the names of prediction tasks in the model. However, its application is only limited to the handling of textual inputs such as medical notes. In this paper, we propose a label dependent attention model LDAM to 1) improve the interpretability by exploiting Clinical-BERT (a biomedical language model pre-trained on a large clinical corpus) to encode biomedically meaningful features and labels jointly; 2) extend the idea of joint embedding to the processing of time-series data, and develop a multi-modal learning framework for integrating heterogeneous information from medical notes and time-series health status indicators. To demonstrate our method, we apply LDAM to the MIMIC-III dataset to predict different disease risks. We evaluate our method both quantitatively and qualitatively. Specifically, the predictive power of LDAM will be shown, and case studies will be carried out to illustrate its interpretability.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Data Mining (ICDM)
Number of pages10
ISBN (Electronic)9781665423984
ISBN (Print)9781665423991
Publication statusPublished - Dec 2021
EventIEEE International Conference on Data Mining - Auckland, New Zealand
Duration: 7 Dec 202110 Dec 2021

Publication series

NameIEEE International Conference on Data Mining (ICDM)
ISSN (Print)1550-4786
ISSN (Electronic)2374-8486


ConferenceIEEE International Conference on Data Mining
Country/TerritoryNew Zealand

User-Defined Keywords

  • label dependent attention model
  • disease risk prediction
  • Clinical-BERT
  • multimodal electronic health record analys


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