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

Data Collection by Survey01:07

Data Collection by Survey

8.4K
The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
8.4K
Types of Surveys01:27

Types of Surveys

280
Surveys are essential for marking property boundaries near water bodies. Different types of surveys are defined, each with its own function. Land surveys mark the property boundaries, while route surveys determine the position of properties on nearby highways. Topographic surveys create maps by capturing the three-dimensional features of the land. Hydrographic surveys focus on the shapes of underwater areas and the movement of streams through the properties. Mine surveys determine the relative...
280
Assessment of the Cardiovascular System I: Subjective Data01:23

Assessment of the Cardiovascular System I: Subjective Data

668
A thorough health history and physical assessment are essential for identifying cardiovascular disease (CVD) symptoms and distinguishing them from other health issues.
Initial Enquiry
Ask the patient about their primary concern and thoroughly explore all reported symptoms.
Medical History
Investigate past illnesses affecting the cardiovascular system, such as angina, anemia, rheumatic fever, congenital heart disease, stroke, thrombophlebitis, dysrhythmias, varicosities
Inquire about symptoms...
668
SBAR II: Application of SBAR01:14

SBAR II: Application of SBAR

5.5K
SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
SBAR Report from a Nurse to a Health Care Provider
S: "Hello, Dr. Smith. This is Jane, RN, from the Med Surg unit. I am calling to tell you about Ms. White in Room 210, who is experiencing increased pain and redness at her incision site. Her recent...
5.5K
Assessment of the Gastrointestinal System II: Health Perception Pattern01:29

Assessment of the Gastrointestinal System II: Health Perception Pattern

379
Assessing the gastrointestinal (GI) system is a complex process that begins with collecting subjective data. This data, collected through patient interviews, provides crucial insights into the patient's health history, perception patterns, and lifestyle habits, all contributing significantly to GI health.
Health Perception Patterns
Health perception patterns offer valuable insights into a patient's lifestyle habits and how they may impact their GI health. These patterns include:
379
Data Collection I01:30

Data Collection I

7.7K
Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
7.7K

You might also read

Related Articles

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

Sort by
Same author

Effects of red palm oil intervention on serum lipids, hepatic antioxidant capacity and gut microbiota in high-fat diet-fed mice.

Open life sciences·2026
Same author

Portraying Ethical Risks of Medical AI: Mixed Methods Study From Connotation Definition to a Survey on Physicians' Cognition.

Journal of medical Internet research·2026
Same author

Genetic Mutation and Epigenetic Silencing Drive Antigen-Negative Relapse in CD7 CAR-T Treated T-cell Lymphoid Malignancies.

Blood cancer discovery·2026
Same author

Spatial Epidemiology of the Ischemic Heart Disease-Asthma Comorbidity: A Global Analysis of Burden Patterns, Risk Drivers, and a Composite Risk Index.

Risk management and healthcare policy·2026
Same author

Efficacy and optimal timing of sivelestat in critically ill patients with COVID-19: a multicenter retrospective cohort study.

BMC pulmonary medicine·2026
Same author

Karacoline attenuates sepsis-induced acute lung injury by suppressing apoptosis via PPARγ-associated inhibition of JNK/ERK MAPK signaling.

Respiratory research·2026

Related Experiment Video

Updated: Dec 17, 2025

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
04:19

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Published on: May 10, 2022

4.2K

Clinical questionnaire filling based on question answering framework.

Jiangtao Ren1, Naiyin Liu1, Xiaojing Wu1

  • 1School of Data and Computer Science, Guangdong Province Key Lab of Computational Science, Sun Yat-sen University, Guangdong 510275, People's Republic of China.

International Journal of Medical Informatics
|June 27, 2020
PubMed
Summary

This study introduces a novel deep learning model for automatically completing medical questionnaires from electronic health records (EHRs). The question answering (QA) framework significantly reduces physician workload by converting unstructured text to structured data efficiently.

Keywords:
EHRInformation extractionMedical textMulti-label classificationQuestion answering

More Related Videos

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

896
Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

4.0K

Related Experiment Videos

Last Updated: Dec 17, 2025

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
04:19

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Published on: May 10, 2022

4.2K
Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

896
Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

4.0K

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Natural Language Processing

Background:

  • Electronic Health Records (EHRs) contain valuable data but are challenging to analyze directly.
  • Physicians manually convert EHR text data to structured formats using time-consuming questionnaires.
  • Automating questionnaire completion is crucial for efficient medical research and data utilization.

Purpose of the Study:

  • To develop a deep learning model capable of automatically completing medical questionnaires using provided text data.
  • To address the limitations of conventional classification methods in handling complex questionnaire structures.
  • To leverage a question answering (QA) framework for efficient and accurate questionnaire completion.

Main Methods:

  • Proposed a neural network model based on a question answering (QA) framework.
  • Treated questionnaire completion as a classification problem, utilizing question information.
  • Developed a single model to fill out entire questionnaires, overcoming resource limitations of conventional approaches.

Main Results:

  • Achieved high F1 scores on three real-world Chinese medical datasets: 0.9273, 0.8834, and 0.9846.
  • The proposed QA model outperformed several baseline models in questionnaire completion tasks.
  • Demonstrated the effectiveness of the QA framework for medical information extraction.

Conclusions:

  • The developed QA model shows strong performance in automatically completing medical questionnaires.
  • This technology can facilitate the creation of systems to assist physicians in data structuring.
  • The system has the potential to significantly reduce physician workload and improve data accessibility for research.