Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Peripheral Arterial Disease II: Clinical Manifestations and Diagnostic Evaluation01:21

Peripheral Arterial Disease II: Clinical Manifestations and Diagnostic Evaluation

Clinical manifestationsPeripheral Arterial Disease (PAD) manifests through a range of symptoms, from the characteristic intermittent claudication to atypical presentations and severe complications in advanced stages. Intermittent claudication, a hallmark symptom of PAD, presents as exercise-induced muscle pain that typically resolves within minutes of rest. This pain is reproducible and stems from inadequate blood flow, leading to the accumulation of lactic acid produced during anaerobic...
Peripheral Artery Disease III: Interprofessional Care01:27

Peripheral Artery Disease III: Interprofessional Care

Peripheral Artery Disease (PAD) is characterized by narrowed arteries that diminish blood flow to the extremities. Effective management of PAD requires an interprofessional approach involving various healthcare professionals. The critical aspects of interprofessional care for PAD patients focus on risk factor modification, drug therapy, exercise therapy, nutrition therapy, critical limb ischemia care, and interventional radiology and surgical procedures.The primary treatment goal for PAD...
Peripheral Artery Disease IV: Nursing Management01:26

Peripheral Artery Disease IV: Nursing Management

The nursing management of a patient with peripheral artery disease (PAD) begins with a thorough assessment of the patient’s health history and clinical manifestations.AssessmentHealth History: Evaluate the patient’s history of hypertension, hyperlipidemia, family history of cardiovascular issues, and lifestyle factors such as dietary patterns, smoking, and physical activity.Physical Examination:Assess the affected extremity for decreased or absent peripheral pulses, temperature changes,...
Peripheral Artery Disease I: Introduction01:30

Peripheral Artery Disease I: Introduction

Peripheral artery disease (PAD) predominantly results from atherosclerosis, which involves the accumulation of fatty deposits, or plaques, within the walls of arteries. This causes them to narrow and harden, significantly reducing blood flow. PAD predominantly affects the legs, particularly the arteries supplying the thighs and calves. In rare cases, it may involve other arteries, including those in the arms.Etiology of PAD:The principal cause of PAD is atherosclerosis, which results from fatty...
Peripheral Artery Disease V: Postoperative Nursing Management01:23

Peripheral Artery Disease V: Postoperative Nursing Management

During the postoperative period, it is crucial to focus on maintaining circulation, identifying and managing potential complications, and planning for discharge.Nursing AssessmentVital signs monitoring: Regularly monitor vital signs, including blood pressure, heart rate, respiratory rate, and temperature, to detect early signs of complications such as bleeding and infection.Circulation assessment: Monitor pulses, perform Doppler assessments, and check capillary refill, color, temperature, and...
Atherosclerosis II: Clinical Manifestations and Diagnostic Tests01:27

Atherosclerosis II: Clinical Manifestations and Diagnostic Tests

Atherosclerosis is a progressive disorder that leads to the thickening and narrowing of arterial walls due to plaque buildup. This condition can cause various symptoms depending on the arteries affected:Coronary Artery Disease (CAD): This condition affects the coronary arteries and may lead to chest pain (angina), shortness of breath (dyspnea), heart attacks, and other heart disease symptoms.Cerebrovascular Disease: This affects blood flow to the brain, causing transient ischemic attacks (TIAs)...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Large language models require a new form of oversight: capability-based monitoring.

NPJ digital medicine·2026
Same author

Multi-scale data improves performance of machine learning model for long COVID identification.

Communications medicine·2026
Same author

An agentic AI system enhances clinical detection of immunotherapy toxicities: a multi-phase validation study.

medRxiv : the preprint server for health sciences·2026
Same author

Governing real-world health data as a public utility.

Science (New York, N.Y.)·2026
Same author

LinkML: an open data modeling framework.

GigaScience·2025
Same author

Development of a robust corpus for automated evaluation of online health information in Chinese using the DISCERN scale.

Journal of the American Medical Informatics Association : JAMIA·2025

Related Experiment Video

Updated: Jun 4, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Discovering peripheral arterial disease cases from radiology notes using natural language processing.

Guergana K Savova1, Jin Fan, Zi Ye

  • 1Division of Biomedical Statistics and Informatics.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|February 25, 2011
PubMed
Summary
This summary is machine-generated.

This study evaluated an open-source clinical Natural Language Processing system for identifying peripheral arterial disease cases in radiology reports. The system achieved high accuracy, demonstrating its effectiveness in clinical text analysis.

More Related Videos

Reduction of Radiation Exposure during Endovascular Treatment of Peripheral Arterial Disease Combining Fiber Optic RealShape Technology and Intravascular Ultrasound
13:48

Reduction of Radiation Exposure during Endovascular Treatment of Peripheral Arterial Disease Combining Fiber Optic RealShape Technology and Intravascular Ultrasound

Published on: April 21, 2023

Related Experiment Videos

Last Updated: Jun 4, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Reduction of Radiation Exposure during Endovascular Treatment of Peripheral Arterial Disease Combining Fiber Optic RealShape Technology and Intravascular Ultrasound
13:48

Reduction of Radiation Exposure during Endovascular Treatment of Peripheral Arterial Disease Combining Fiber Optic RealShape Technology and Intravascular Ultrasound

Published on: April 21, 2023

Area of Science:

  • Medical Informatics
  • Clinical Natural Language Processing
  • Health Informatics

Background:

  • Electronic Medical Records (EMR) contain valuable clinical information.
  • Automated analysis of EMR data is crucial for efficient healthcare.
  • Peripheral arterial disease (PAD) diagnosis can be challenging.

Purpose of the Study:

  • To evaluate an open-source clinical Natural Language Processing (NLP) system for identifying PAD cases.
  • To assess the system's performance against a manually curated gold standard.
  • To determine the accuracy, sensitivity, and specificity of the NLP system in classifying PAD cases.

Main Methods:

  • Applied and extended Mayo's Clinical Text Analysis and Knowledge Extraction System (cTAKES).
  • Utilized radiology reports for peripheral arterial disease (PAD) case discovery.
  • Compared system performance against a gold standard of 223 positive, 19 negative, 63 probable, and 150 unknown cases.

Main Results:

  • Achieved an overall accuracy agreement of 0.93, significantly outperforming a baseline of 0.46.
  • Demonstrated high sensitivity (0.93-0.96) for positive, probable, and unknown PAD cases.
  • Reported high specificity and negative predictive values (in the 90s) across all categories.

Conclusions:

  • The evaluated open-source clinical NLP system shows high accuracy and effectiveness for PAD case discovery from radiology reports.
  • The system's performance suggests its utility in enhancing clinical data analysis and research.
  • Further improvements can be made by addressing identified error sources in NLP applications.