ASN's Mission

To create a world without kidney diseases, the ASN Alliance for Kidney Health elevates care by educating and informing, driving breakthroughs and innovation, and advocating for policies that create transformative changes in kidney medicine throughout the world.

learn more

Contact ASN

1401 H St, NW, Ste 900, Washington, DC 20005


The Latest on X

Kidney Week

Please note that you are viewing an archived section from 2023 and some content may be unavailable. To unlock all content for 2023, please visit the archives.

Abstract: TH-PO260

Real-Time Dual Prediction of Intradialytic Hypotension and Hypertension Using an Explainable Deep Learning Model

Session Information

Category: Dialysis

  • 801 Dialysis: Hemodialysis and Frequent Dialysis


  • Yun, Donghwan, Seoul National University Hospital, Jongno-gu, Seoul, Korea (the Republic of)
  • Kim, Dong Ki, Seoul National University Hospital, Jongno-gu, Seoul, Korea (the Republic of)
  • Oh, Kook-Hwan, Seoul National University Hospital, Jongno-gu, Seoul, Korea (the Republic of)
  • Han, Seung Seok, Seoul National University Hospital, Jongno-gu, Seoul, Korea (the Republic of)

Intradialytic hypotension (IDH) and hypertension (IDHTN) are associated with poor outcomes in hemodialysis patients. However, there is currently no real-time predictive model for dual outcomes. This study aims to develop an explainable deep learning model using a sequence-to-sequence-based attention network to simultaneously predict IDH and IDHTN.


Electronic health records of 11,110 hemodialysis patients were utilized, comprising 302,774 sessions. The data was divided into training (70%), validation (10%), and test (20%) sets using randomization. IDH-1 was defined as nadir systolic blood pressure (BP) <90 mmHg, IDH-2 as a decrease in systolic BP ≥20 mmHg and/or a decrease in mean arterial pressure ≥10 mmHg, and IDHTN as an increase in systolic BP ≥10 mmHg within 1 hour. The temporal fusion transformer (TFT)-based model was developed and compared with other machine learning models, including recurrent neural network, light gradient boosting machine, random forest, and logistic regression, in terms of model performance measured by receiver operating characteristic curve (AUROC) and area under the precision-recall curves (AUPRC).


The TFT-based model outperformed other models with AUROCs of 0.953 (0.952–0.954), 0.892 (0.891–0.893), and 0.889 (0.888–0.890) for predicting IDH-1, IDH-2, and IDHTN, respectively. The AUPRCs of the TFT-based model for the outcomes were higher compared to other models. Key predictors included age, previous session, systolic BP, and elapsed time.


The developed TFT-based model enables real-time prediction of both IDH and IDHTN while providing explainable variable importance.

Mean weights of time-invariant and time-varying features from the attention module in the model. (A), Weights of time-invariant features. (B), Weights of time-varying features. BP, blood pressure.