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 

AI use cases

Use case 1. Automated 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.