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

Pharmacovigilance01:19

Pharmacovigilance

876
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...
876
Preclinical Development: Overview01:28

Preclinical Development: Overview

4.5K
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...
4.5K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

757
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
757
Drug Administration and Therapy Phases: Overview01:26

Drug Administration and Therapy Phases: Overview

504
Drugs, the chemical agents used in diagnosing, treating, or preventing diseases, undergo a four-phase process of development: pharmaceutic, pharmacokinetics, pharmacodynamics, and therapeutic.
The pharmaceutical phase focuses on leveraging the physicochemical properties of the drug to design and manufacture an effective product. Variants include orally administered tablets or capsules, topical creams or ointments, and parenteral-delivery solutions or emulsions.
The pharmacokinetic phase...
504
Opioid Analgesics: Morphine and Other Natural Cogeners01:20

Opioid Analgesics: Morphine and Other Natural Cogeners

283
Opioids are a class of drugs that mimic endogenous opioid peptides and act on opioid receptors, and help in pain relief. These compounds are classified as natural, synthetic, or semi-synthetic. Natural opioids, like morphine, codeine, and thebaine, are derived from the opium poppy plant (Papaver somniferum or Papaver album) and are termed opiates. Synthetic opioids are artificial, while semi-synthetic opioids combine natural and synthetic compounds. Morphine, a prototypical opioid, possesses a...
283
Drug Discovery: Overview01:26

Drug Discovery: Overview

8.0K
Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
8.0K

You might also read

Related Articles

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

Sort by
Same author

Short-Term and Long-Term Opioid Prescribing by Specialty, 2010 to 2024.

JAMA network open·2026
Same author

Opioid Use and Pain Resolution for Acute Pain Among Opioid-Naive Patients.

JAMA network open·2026
Same author

Genetic diversity and occurrence of antibiotic resistance genes from community wastewater in Dhaka and Cox's Bazar, Bangladesh.

One health (Amsterdam, Netherlands)·2026
Same author

Platelet-derived non-coding RNAs as emerging contributors to autoimmune inflammation.

Naunyn-Schmiedeberg's archives of pharmacology·2026
Same author

Oxidative Activation of the Heme Nitric Oxide/Oxygen-Binding Protein (H-NOX) from <i>Caulobacter crescentus</i>.

Biochemistry·2025
Same author

Ankylosing spondylitis management: a narrative review of radiofrequency ablation and naproxen.

Pain management·2025

Related Experiment Video

Updated: Jul 15, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.1K

Artificial Intelligence-Enabled Software Prototype to Inform Opioid Pharmacovigilance From Electronic Health Records:

Alfred Sorbello1, Syed Arefinul Haque1, Rashedul Hasan1

  • 1Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States.

JMIR AI
|September 29, 2023
PubMed
Summary

This study developed an AI prototype to detect adverse drug events (ADEs) in electronic health records (EHRs), improving opioid safety signal detection. The tool efficiently extracts valuable information from unstructured EHR data, reducing manual research efforts.

Keywords:
EHRFood and Drug Administrationartificial intelligencedeep learningdrugelectronic health recordsnatural languagepharmacovigilancereal world datasoftware application

More Related Videos

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

9.7K
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

4.6K

Related Experiment Videos

Last Updated: Jul 15, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.1K
Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

9.7K
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

4.6K

Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Pharmacovigilance

Background:

  • Electronic health records (EHRs) contain valuable patient data for drug safety.
  • Artificial intelligence (AI) and natural language processing (NLP) can extract insights from unstructured EHR text.
  • Detecting safety signals for US Food and Drug Administration (FDA)-regulated drugs can be enhanced by EHR data.

Purpose of the Study:

  • To develop an AI-enabled software prototype for identifying adverse drug event (ADE) safety signals.
  • To extract ADE signals from free-text discharge summaries in EHRs.
  • To improve opioid drug safety and support FDA research activities.

Main Methods:

  • Developed a web-based software prototype using keyword/trigger-phrase searching, rule-based algorithms, and deep learning.
  • Utilized MedSpacy for section identification and Spark NLP for Healthcare for named entity recognition in discharge summaries.
  • Extracted candidate ADEs for opioid drugs from the MIMIC III database and gathered feedback from 15 FDA staff members.

Main Results:

  • Successfully identified known, opioid-related adverse drug reactions from EHR text.
  • Achieved AI model performance metrics: accuracy (0.66), recall (0.69), precision (0.64), and F1-score (0.67).
  • FDA participants found the prototype highly desirable for usability, visualizations, and potential to support drug safety signal detection, saving time and manual effort.

Conclusions:

  • The novel prototype automates the extraction and analysis of clinically useful information from unstructured EHR text using AI.
  • It enhances efficiency in using real-world data for opioid drug safety monitoring.
  • The tool increases data usability for regulatory review and reduces the manual research burden.