Using Artificial Intelligence in Pharmacovigilance

October 12, 2021

Using Artificial Intelligence in Pharmacovigilance

As we briefly discussed in the previous chapter, Artificial Intelligence (AI) is used to simulate the processes of a human brain with the help of computer systems. It encompasses various technologies, including rule-following, reasoning – using rules to reach rough or specific conclusions, learning, and self-correction.

Implementation areas of AI

In the area of pharmacovigilance electronic systems, AI technologies could be implemented in case collection, consolidated reporting, and signal and quality management.

For example, using machine learning algorithms to automate medical coding according to MedDRA dictionary for medical terms or WHODrug dictionary for medical products.

The image below demonstrates how AI based on a neural network is used to detect and manage signals, find duplicates, and auto-code in the Flex Databases Pharmacovigilance system.

Additionally, AI is used in safety signal detection, including:

  • multimodal signal detection,
  • signal detection using neural networks,
  • predictive signal detection.

In general, AI and other automation technologies in pharmacovigilance allow to eliminate human errors, standardize processes, shorten the processing cycle, and reduce manual work.

Examples of AI technologies 

TechnologyShort descriptionUse cases of pharmacovigilance electronic system 
Machine learning (ML)Machine learning (ML) is a subset of AI, allowing the creation of algorithms that can learn and make predictions based on the data. Instead of following a predefined set of rules and instructions, these algorithms are taught to identify patterns in big data and improve over time and with more data. This technology helps identify potential signals.Case collection, periodic reporting, signal management, and risk management.
Neural networkSystem replicating a neural structure of a mammal’s brain. Neural networks usually consist of multiple layers which are formed by numerous nodes. This technology helps identify potential signals.Case collection, periodic reporting, signal management, and risk management.
Semantic searchSemantic search is meant to improve search accuracy and deliver relevant results by understanding user intentions and search query context.Case collection, periodic reporting, signal management, and risk management.

AI use cases

Use case 1Automated case collection and evaluation of serious adverse events (SAE) from online comments on Indian websites. 

Traditional safety data collection is challenging in this area, however, using of artificial neural networks and sentiment analysis allows to automatically identify characterizations of the researched objects in the emotionally coloured text.

Use case 2. Predicting the safety profile of a developed drug

Researchers combined in vitro data with safety information from FAERS database, and created machine learning models, allowing to predict future adverse events based on the data point from other databases, such as PubMed.

Blog

December 2, 2024
Flex Databases Achieves ISO 27001 Certification

We’re pleased to announce that Flex Databases’ IT security measures are now formally certified under ISO 27001. This certification, earned after a thorough audit, demonstrates that our systems meet internationally recognized standards for information security. ISO 27001 sets requirements for managing sensitive company and customer data securely. By achieving this milestone, we assure our clients […]

November 28, 2024
The Future of AI in TMF

The Trial Master File (TMF) is the cornerstone of clinical trial documentation, serving as the definitive record of compliance with regulatory standards and ethical guidelines. Yet, the management of TMFs has grown increasingly complex with the rise in document volume, diversity, and stringent compliance requirements. Artificial intelligence (AI) is poised to address these challenges, introducing […]

November 19, 2024
How to Choose Pharmacovigilance (PV) Software for Clinical Trial Management

Pharmacovigilance (PV) is a critical aspect of drug safety, focusing on detecting, assessing, understanding, and preventing adverse drug reactions or other drug-related problems. As global regulatory requirements for drug safety evolve, adopting reliable pharmacovigilance software has become essential for ensuring compliance and improving operational efficiency. Selecting the right PV software can significantly impact your organization’s […]

November 14, 2024
How to Integrate CTMS with EDC

Clinical Trial Management Systems (CTMS) and Electronic Data Capture (EDC) systems are critical to the effective management of clinical trials. By integrating CTMS and EDC, organizations can streamline processes, enhance data quality, and improve overall efficiency. Here are five key tips to ensure a successful CTMS and EDC integration. Effortless Integration of Flex Databases CTMS […]

Contact us

Get in touch to discuss compliance, implementation, demos, pricing

We are here for all of your questions! Tell us more about yourself and we will organize a tailored live demo to show how you can power up your clinical trials processes with Flex Databases.