Development and Validation of an Explainable Machine Learning Model for  Early Prediction of Intradialytic Hypotension by Integrating Antihypertensive  Medication Profiles and Clinical Parameters in Maintenance Hemodialysis  Patients: A Prospective Cohort

Ali Saqlain Haider, Samee Ullah Khan, Sadaf Asim, Sarah Zaheer, Irfan Ahmad, Fariha Ahmad Khan

Development and Validation of an Explainable Machine Learning Model for Early Prediction of Intradialytic Hypotension by Integrating Antihypertensive Medication Profiles and Clinical Parameters in Maintenance Hemodialysis Patients: A Prospective Cohort

Authors

Ali Saqlain Haider, Samee Ullah Khan, Sadaf Asim, Sarah Zaheer, Irfan Ahmad, Fariha Ahmad Khan

Keywords:

Intradialyticlearning, hypotension,XGBoost,SHAP explainability, hemodialysis, antihypertensive timing, predictive modeling.

Abstract

Intradialytic hypotension (IDH) is a majorcomplication of maintenance hemodialysis(MHD), strongly associated with vascularaccess thrombosis, cardiovascular events,and mortality. Existing predictive toolsfrequentlyoverloodetailedantihypertensive drug timing and lack explainability.

References

1. Flythe, M. D., Xue, H., Boblitz, A., Lynn, H., & Brunelli, S. M. (2015). Intradialytic hypotension and its association with all-cause and cardiovascular mortality. American Journal of Kidney Diseases, 65(1), 100–109.

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Published

2026-09-01

How to Cite

Ali Saqlain Haider, Samee Ullah Khan, Sadaf Asim, Sarah Zaheer, Irfan Ahmad, Fariha Ahmad Khan. (2026). Development and Validation of an Explainable Machine Learning Model for Early Prediction of Intradialytic Hypotension by Integrating Antihypertensive Medication Profiles and Clinical Parameters in Maintenance Hemodialysis Patients: A Prospective Cohort. International Journal of Pharmacy Research & Technology (IJPRT), 16(2), 4066–4076. Retrieved from https://ijprt.org/index.php/pub/article/view/2968