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

Introduction To Survival Analysis01:18

Introduction To Survival Analysis

596
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
596
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.2K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.2K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

463
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
463
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

405
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
405
Clinical Trials01:16

Clinical Trials

10.1K
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...
10.1K
Purpose of Health Records II01:19

Purpose of Health Records II

1.3K
Health records serve various essential purposes in the healthcare system. Here are some key purposes:
1.3K

You might also read

Related Articles

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

Sort by
Same author

Expression of tissue factor pathway inhibitor-2 in gastric stromal tumor and its clinical significance.

Experimental and therapeutic medicine·2014
Same author

Facile access to cytocompatible multicompartment micelles with adjustable Janus-cores from A-block-B-graft-C terpolymers prepared by combination of ROP and ATRP.

Colloids and surfaces. B, Biointerfaces·2014
Same author

Functional layers for Zn(II) ion detection: from molecular design to optical fiber sensors.

The journal of physical chemistry. B·2013
Same author

Expression of the 78 kD glucose-regulated protein is induced by endoplasmic reticulum stress in the development of hepatopulmonary syndrome.

Gene·2013
Same author

Multi-nuclear silver(I) and copper(I) complexes: a novel bonding mode for bispyridylpyrrolides.

Dalton transactions (Cambridge, England : 2003)·2013
Same author

Transcriptome profilings of female Schistosoma japonicum reveal significant differential expression of genes after pairing.

Parasitology research·2013

Related Experiment Video

Updated: Dec 11, 2025

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

16.3K

A system for automatically extracting clinical events with temporal information.

Zhijing Li1,2, Chen Li3,4, Yu Long1,2

  • 1School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.

BMC Medical Informatics and Decision Making
|August 22, 2020
PubMed
Summary

This study introduces a recurrent neural network for automatically extracting clinical events and their temporal information from medical records. The system achieves state-of-the-art performance and demonstrates domain adaptation capabilities for improved health informatics research.

Keywords:
Attention mechanismClinical text miningEvent extractionPiecewise representationRelation extractionTemporal extraction

More Related Videos

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients
09:00

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients

Published on: April 13, 2021

5.0K
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.7K

Related Experiment Videos

Last Updated: Dec 11, 2025

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

16.3K
TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients
09:00

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients

Published on: April 13, 2021

5.0K
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.7K

Area of Science:

  • Health Informatics
  • Natural Language Processing
  • Machine Learning

Background:

  • The increasing volume of health data necessitates efficient methods for extracting clinical events.
  • Manual extraction of temporal event information from clinical records is time-consuming and complex.
  • Automated extraction is crucial for advanced health applications and research.

Purpose of the Study:

  • To develop an automated system for extracting clinical event expressions and their temporal information.
  • To leverage recurrent neural networks and attention mechanisms for improved accuracy.
  • To enable domain adaptation for diverse clinical data types.

Main Methods:

  • Utilized a recurrent neural network architecture with attention-based and recursive networks.
  • Incorporated a piecewise sentence representation to enhance information utilization.
  • Integrated semantic information using word representations from BioASQ and Wikipedia.

Main Results:

  • The system achieved state-of-the-art performance on the THYME corpus.
  • Evaluation on the TimeBank_Dense corpus further validated the system's effectiveness.
  • Demonstrated successful domain adaptation, performing well on different cancer datasets.

Conclusions:

  • The developed system effectively extracts and links temporal and event expressions from unstructured clinical records.
  • Combining piecewise representation with attention mechanisms enhances feature capture.
  • The system is flexible, adaptable to various document types, and supports domain transfer.