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

Classification of Illness01:17

Classification of Illness

9.1K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
9.1K
Feedback control systems01:26

Feedback control systems

753
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
753

You might also read

Related Articles

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

Sort by
Same author

Inequalities in cancer diagnostic outcomes for patients with a learning disability: a retrospective cohort study in England.

BMJ oncology·2026
Same author

Factors Influencing the Use of Mobile Apps and Wearables: Pre- and Post-Surgery Quality of Life Assessment Study.

JMIR formative research·2026
Same author

Exploring Grassroots Indicators for Pandemic Prevention, Preparedness, and Response: A Systematic Narrative Review.

International journal of health policy and management·2026
Same author

Development of the 'COuld it Be RA' (COBRA) tool to facilitate early identification of people at risk of developing rheumatoid arthritis in primary care.

RMD open·2026
Same author

International survey of people living with chronic conditions: development and evaluation of the PaRIS Patient Questionnaire (PaRIS-PQ) in 18 countries.

BMJ quality & safety·2025
Same author

Perceived mental health literacy as a mediator between cognitive behavioural therapy (CBT) and depressive symptoms: a secondary data analysis of CoBalT trial data.

Cognitive behaviour therapy·2025

Related Experiment Video

Updated: Mar 6, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.9K

Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level

Chris Gibbons1,2, Suzanne Richards3, Jose Maria Valderas4

  • 1Centre for Health Services Research, University of Cambridge, Cambridge, United Kingdom.

Journal of Medical Internet Research
|March 17, 2017
PubMed
Summary
This summary is machine-generated.

Machine learning accurately classifies doctor performance feedback, with "respected," "professional," and "interpersonal" comments indicating higher performance. This aids quality assurance and professional development.

Keywords:
data miningfeedbackmachine learningsurveys and questionnaireswork performance

Related Experiment Videos

Last Updated: Mar 6, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.9K

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Professional Performance Evaluation

Background:

  • Machine learning (ML) offers efficient classification of open-text doctor performance reports.
  • Applications include quality assurance, patient safety, and continuing professional development.

Purpose of the Study:

  • Evaluate ML algorithm accuracy in classifying doctor performance reports.
  • Assess if ML classifications identify significant differences in UK doctors' professional performance.

Main Methods:

  • Analyzed 1636 open-text comments from 548 doctors via the General Medical Council Colleague Questionnaire (GMC-CQ).
  • Coded comments into 5 themes: innovation, interpersonal skills, popularity, professionalism, respect.
  • Trained 8 ML algorithms for comment classification, assessing performance with cross-validation.
  • Compared GMC-CQ scores between doctors based on ML classifications.

Main Results:

  • ML algorithms achieved high performance (F-score .68–.83).
  • Ensemble ML achieved a mean human-computer interrater agreement of .88.
  • Comments classified as "respected," "professional," and "interpersonal" correlated with higher GMC-CQ scores (P<.05).

Conclusions:

  • ML algorithms effectively classify open-text doctor performance feedback into human-derived themes.
  • Respect, professionalism, and interpersonal skills in feedback are key indicators of doctor performance.