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

Drug Nomenclature01:17

Drug Nomenclature

1.7K
During the development of a new pharmaceutical, the manufacturer initially assigns a code name to the drug. Once approved, the drug receives a United States Adopted Name (USAN)—a generic, nonproprietary designation. Upon being listed in the United States Pharmacopeia, this nonproprietary name becomes the drug's official name. Additionally, the manufacturer assigns a proprietary name or trademark, which serves as the brand name under which the drug is marketed. It is worth noting that...
1.7K
Purpose of Health Records I01:11

Purpose of Health Records I

1.2K
The vital purpose of health records is to provide a complete and accurate account of a patient's medical history, including communication, diagnostic and therapeutic orders, care planning, research, and quality review.
Here's a breakdown of how health records serve these purposes:
1.2K
Nursing Clinical Information System01:27

Nursing Clinical Information System

765
Nursing Clinical Information System (NCIS)
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
Critical attributes of NCIS include:
765
Methods of Documentation II: POMR01:26

Methods of Documentation II: POMR

938
The Problem-Oriented Medical Record (POMR) revolutionized medical record-keeping by introducing a systematic approach focusing on the patient's problems rather than merely listing symptoms. Dr. Lawrence Weed's introduction of this method in the 1960s marked a significant advancement in medical documentation. The POMR framework consists of four key components: the database, problem list, plan of care, and progress notes.
938
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

252
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
252
Drug Elimination: Non-Renal Routes01:23

Drug Elimination: Non-Renal Routes

2.3K
The liver plays a pivotal role in eliminating drugs and their metabolites, primarily through a process known as biliary excretion. This process involves the hepatocytes, the primary cells in the liver that generate bile. A range of transporters actively expels polar drugs or hydrophilic drug metabolites into the bile, which transports the drugs and metabolites into the small intestine. From here, they are eventually expelled from the body through feces. In some instances, the original drug or a...
2.3K

You might also read

Related Articles

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

Sort by
Same author

Emotion Classification in Japanese Cancer Survivor Interview Narratives Using Sentiment Polarity and Plutchik Emotion Frameworks: Model Development and Evaluation Study.

JMIR formative research·2026
Same author

A Multiassessment and Multiprofessional Agents Approach for Medical Chatbot Risk Estimation: Development and Evaluation Study.

JMIR medical informatics·2026
Same author

Comparison of Mask-Wearing Behavior on Social Media and Its Relationship With Demographic Characteristics During the COVID-19 Pandemic: Social Media Data Analysis Between the United States and Japan.

JMIR human factors·2026
Same author

Comparative Analysis of Japanese Clinical Note Styles Between Physicians and Large Language Models Using Identical Psychiatric Cases: Quantitative Text Analysis.

JMIR formative research·2026
Same author

Health-Related Quality of Life Before and After Sobriety in Combination With an Adjunctive Journaling App in Patients With Alcohol-Related Liver Disease: Prospective Single-Arm Study.

JMIR formative research·2026
Same author

Social harmony at work: A sharedness index linking team atmosphere to individual well-being in a Japanese company.

PloS one·2025

Related Experiment Video

Updated: Jun 24, 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

15.9K

Using the Natural Language Processing System Medical Named Entity Recognition-Japanese to Analyze Pharmaceutical Care

Yukiko Ohno1, Riri Kato1, Haruki Ishikawa1

  • 1Faculty of Pharmacy, Keio University, Tokyo, Japan.

JMIR Formative Research
|June 4, 2024
PubMed
Summary
This summary is machine-generated.

This study explored using AI to extract patient disease and symptom data from pharmaceutical records. The Medical Named Entity Recognition-Japanese (MedNER-J) system performed best on assessment data, but needs improvement for other record types.

Keywords:
EMRJapaneseNLPcefazolin sodiumelectronic medical recordextractionmachine learningnamed entity recognitionnatural language processingpharmaceutical care records

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

8.7K
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

4.5K

Related Experiment Videos

Last Updated: Jun 24, 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

15.9K
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

8.7K
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

4.5K

Area of Science:

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

Background:

  • Large language models (LLMs) advance AI, enabling medical information extraction from unstructured data like patient records.
  • Named Entity Recognition (NER) is established for physician records but underutilized in pharmaceutical care documentation.

Purpose of the Study:

  • To assess the feasibility of automatically extracting patient disease and symptom information from pharmaceutical care records.
  • To evaluate the performance of Medical Named Entity Recognition-Japanese (MedNER-J), a system for physician records, on pharmaceutical data.

Main Methods:

  • Applied MedNER-J to subjective, objective, assessment, and plan (SOAP) data from 49 patients' pharmaceutical care records.
  • Evaluated MedNER-J performance using precision, recall, and F1-score metrics.

Main Results:

  • MedNER-J achieved higher F1-scores for objective (0.70) and assessment (0.76) data compared to subjective (0.46) and plan (0.35) data.
  • The system demonstrated strong performance in NER and positive-negative classification for assessment data (F1-score=0.64).
  • Higher performance on objective and assessment data is attributed to their technical terminology aligning with MedNER-J's training data.

Conclusions:

  • MedNER-J successfully processed pharmaceutical care records, with optimal performance on assessment data.
  • Challenges persist in analyzing non-assessment data, necessitating enhanced training datasets, particularly for subjective information.
  • Further development is required to fully implement MedNER-J for comprehensive pharmaceutical care record analysis.