Related Experiment Video
Updated: Dec 17, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
Automatic Extraction of Risk Factors for Dialysis Patients from Clinical Notes Using Natural Language Processing
George Michalopoulos1, Hammad Qazi1, Alexander Wong1
1University of Waterloo.
Abstract:
Studies have shown that mental health and comorbidities such as dementia, diabetes and cardiovascular diseases are risk factors for dialysis patients. Extracting accurate and timely information associated with these risk factors in the patient health records is not only important for dialysis patient management, but also for real-world evidence generation. We presented HERALD, an natural language processing (NLP) system for extracting information related to risk factors of dialysis patients from free-text progress notes in an electronic dialysis patient management system. By converting semi-structured notes into complete sentences before feeding them into the NLP module, the HERALD system was able achieved 99%, 83% and 80% accuracy in identifying dementia, diabetes and infarction, respectively.
More Related Videos
Related Concept Videos
Chronic Kidney Disease I: Introduction
Dialysis
Acute kidney injury develops suddenly and can be caused by pre-renal causes (e.g., hypovolemia, shock), intrinsic renal causes (e.g., acute tubular necrosis), or post-renal causes (e.g., urinary obstruction). In contrast, chronic renal failure progresses gradually over time and is often...
Hemodialysis III: Nursing Management
Extracorporeal Removal of Drugs: Peritoneal Dialysis and Hemodialysis
Chronic Kidney Disease III: Interprofessional Care
Acute Kidney Injury V: Interprofessional Care

