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Results for "Acute Kidney Injury"

Community-Based Early-Stage Chronic Kidney Disease Screening using Explainable Machine Learning for Low-Resource Settings

arXiv ·

This paper introduces an explainable machine learning framework for early-stage chronic kidney disease (CKD) screening, specifically designed for low-resource settings in Bangladesh and South Asia. The framework utilizes a community-based dataset from Bangladesh and evaluates multiple ML classifiers with feature selection techniques. Results show that the ML models achieve high accuracy and sensitivity, outperforming existing screening tools and demonstrating strong generalizability across independent datasets from India, the UAE, and Bangladesh.

A graduate’s view on revealing invisible data

MBZUAI ·

MBZUAI graduate Svetlana Maslenkova worked with Assistant Professor Mohammad Yaqub on a project focused on the earlier detection of kidney failure using tabular data. Maslenkova's master's thesis involved predicting Acute Kidney Injury (AKI) using Electronic Health Records (EHR), specifically the MIMIC-IV v2.0 database. She found that patient weight distribution was a factor in the severity of kidney failure. Why it matters: This research highlights the potential of AI and machine learning to improve healthcare outcomes through the analysis of often-overlooked tabular data in electronic health records.