Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

Classification of Illness

Classification of Illness

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...

You might also read

Related Articles

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

Sort by

Same author

Oncological Safety of Minimally Invasive Multivisceral Resection for cT4b Rectal Cancer: A Multicenter Comparison With Open Surgery.

In vivo (Athens, Greece)·2026

Same author

Exploring plasma cell-free RNA biomarkers for predicting immune checkpoint inhibitor efficacy in gastric cancer: a translational sub-study of the KSCC VOYAGER phase II trial.

International journal of clinical oncology·2026

Same author

Impact of long COVID on health-related quality-of-life among Japanese adults: findings of CARE Japan study.

Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation·2026

Same author

Protective Effects of a New Human Placental Extract Against Hair Graying and Chemotherapy-Induced Peripheral Neuropathy.

International journal of molecular sciences·2026

Same author

Multimodal GPT-5 for Predicting Poor Functional Outcomes After Intracerebral Hemorrhage in the Emergency Department: Validation Study.

JMIR AI·2026

Same author

Self-reported long COVID symptoms among Japanese adults: Findings of CARE Japan online questionnaire-based prospective cohort study.

Journal of infection and chemotherapy : official journal of the Japan Society of Chemotherapy·2026

Search research articles

Related Experiment Video

Updated: Sep 12, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Usefulness and Challenges in Developing Disease Extraction Algorithms in Japanese Large-Scale Data-Driven Databases.

Naoichi Sato¹, Rieko Izukura², Shinichiro Ikeda¹

¹Medical Information Center, Kyushu University Hospital, Fukuoka.

Studies in Health Technology and Informatics

|August 8, 2025

Summary

This summary is machine-generated.

Researchers developed algorithms to accurately identify diseases in electronic health records (EHRs). This study validated definitions for six diseases in a large Japanese database, aiding future large-scale clinical research and drug safety studies.

Keywords:

APC definition MID-NET Outcome definition

More Related Videos

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

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

Published on: April 13, 2021

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

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

Published on: September 20, 2018

Related Experiment Videos

Last Updated: Sep 12, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

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

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

Published on: April 13, 2021

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

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

Published on: September 20, 2018

Area of Science:

Medical Informatics
Clinical Epidemiology
Health Data Science

Background:

Electronic health records (EHRs) lack direct disease pathology data, necessitating accurate extraction algorithms.
Developing reliable methods to identify diseases within EHRs is crucial for clinical research and drug safety.

Purpose of the Study:

To create and validate outcome definitions for identifying six specific diseases within the MID-NET database.
To assess the practicality and performance of these definitions in a large-scale Japanese clinical database.

Main Methods:

Development and validation of outcome definitions for six diseases.
Utilizing MID-NET, a Japanese large-scale database for data-driven medical studies.
Evaluation of positive predictive values (PPVs) and sensitivities across different medical facilities.

Main Results:

Successfully created practical outcome definitions for identifying six diseases in EHRs.
Observed variations in PPVs and sensitivities across medical facilities, highlighting implementation challenges.
Demonstrated the feasibility of using these definitions for large-scale data analysis.

Conclusions:

The developed outcome definitions are practical for large-scale EHR databases.
Identifying facility-specific performance variations is key to refining definition creation and operation.
This work contributes to the development of standardized, transnational outcome definitions for clinical research.