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

Clinical Trials: Overview01:11

Clinical Trials: Overview

5.4K
Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
5.4K
Clinical Trials01:16

Clinical Trials

11.1K
Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
11.1K
Chi-square Analysis02:46

Chi-square Analysis

44.7K
The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
44.7K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

520
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
520
Preclinical Development: Overview01:28

Preclinical Development: Overview

6.4K
Preclinical development consists of a series of tests that ensure the safety and efficacy of a new therapeutic compound before it is tested in humans. There are four main phases to this process. First, safety pharmacology tests are conducted to ensure the drug does not produce any acutely harmful effects. These tests examine parameters such as bronchoconstriction, cardiac dysrhythmias, blood pressure changes, and ataxia. Next, preliminary toxicological testing is performed to determine the...
6.4K
Pharmacovigilance01:19

Pharmacovigilance

1.9K
Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...
1.9K

You might also read

Related Articles

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

Sort by
Same author

Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network.

Studies in health technology and informatics·2015
Same author

Treatment of gummy smile: Nasal septum dysplasia as etiologic factor and therapeutic target.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS·2015
Same author

Mesoporous TiO2/Zn2Ti3O8 hybrid films synthesized by polymeric micelle assembly.

Chemical communications (Cambridge, England)·2015
Same author

Inhibition of HIV Expression and Integration in Macrophages by Methylglyoxal-Bis-Guanylhydrazone.

Journal of virology·2015
Same author

Ease of adoption of clinical natural language processing software: An evaluation of five systems.

Journal of biomedical informatics·2015
Same author

Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2.

Journal of biomedical informatics·2015

Related Experiment Video

Updated: Mar 24, 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

16.6K

Citation Sentiment Analysis in Clinical Trial Papers.

Jun Xu1, Yaoyun Zhang1, Yonghui Wu1

  • 1School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|March 10, 2016
PubMed
Summary

This study introduces citation sentiment analysis for biomedical papers. Machine learning effectively identifies sentiment in citations, aiding reproducibility assessments.

More Related Videos

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

9.3K
In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

2.4K

Related Experiment Videos

Last Updated: Mar 24, 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

16.6K
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

9.3K
In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

2.4K

Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Scientific Communication

Background:

  • Scientific publications often contain subjective statements about cited works, influencing reproducibility.
  • Citation sentiment analysis aims to automatically detect the polarity of these statements.
  • Understanding citation sentiment is crucial for evaluating scientific literature.

Purpose of the Study:

  • To develop and evaluate machine learning models for citation sentiment analysis in biomedical publications, specifically clinical trials.
  • To assess the effectiveness of various features, including n-grams, sentiment lexicons, and structural information.
  • To compare citation sentiment analysis in the biomedical domain with general domains.

Main Methods:

  • Annotation of a citation sentiment corpus using clinical trial papers.
  • Examination of word n-gram features, sentiment lexicon features, and problem-specific structure features.
  • Application of machine learning techniques to classify citation sentiment polarity.

Main Results:

  • Combined features (word n-grams, sentiment lexicons, structure) achieved the highest performance.
  • Micro F-score of 0.860 and Macro F-score of 0.719 were obtained.
  • Machine learning methods are feasible for citation sentiment analysis in biomedical literature.

Conclusions:

  • Citation sentiment analysis is a viable approach for the biomedical domain.
  • Integrated features significantly improve the accuracy of sentiment detection.
  • Unique challenges exist in biomedical citation sentiment analysis compared to general domains.