Artificial Intelligence Within Pharmacovigilance: A Means to Identify Cognitive Services and the Framework for Their Validation

Abstract

Introduction Pharmacovigilance (PV) detects, assesses, and prevents adverse events (AEs) and other drug-related problems by collecting, evaluating, and acting upon AEs. The volume of individual case safety reports (ICSRs) increases yearly, but it is estimated that more than 90% of AEs go unreported. In this landscape, embracing assistive technologies at scale becomes necessary to obtain a higher yield of AEs, to maintain compliance, and transform the PV professional work life. Aim The aim of this study was to identify areas across the PV value chain that can be augmented by cognitive service solutions using the methodologies of contextual analysis and cognitive load theory. It will also provide a framework of how to validate these PV cognitive services leveraging the acceptable quality limit approach. Methods The data used to train the cognitive service were an annotated corpus consisting of 20,000 ICSRS from which we developed a framework to identify and validate 40 cognitive services ranging from information extraction to complex decision making. This framework addresses the following shortcomings: (1) needing subject-matter expertise (SME) to match the artificial intelligence (AI) model predictions to the gold standard, commonly referred to as 'ground truth' in the AI space, (2) ground truth inconsistencies, (3) automated validation of prediction missing context, and (4) auto-labeling causing inaccurate test accuracy. The method consists of (1) conducting contextual analysis, (2) assessing human cognitive workload, (3) determining decision points for applying artificial intelligence (AI), (4) defining the scope of the data, or annotated corpus required for training and validation of the cognitive services, (5) identifying and standardizing PV knowledge elements, (6) developing cognitive services, and (7) reviewing and validating cognitive services. Results By applying the framework, we (1) identified 51 decision points as candidates for AI use, (2) standardized the process to make PV knowledge explicit, (3) embedded SMEs in the process to preserve PV knowledge and context, (4) standardized acceptability by using established quality inspection principles, and (5) validated a total of 126 cognitive services. Conclusion The value of using AI methodologies in PV is compelling; however, as PV is highly regulated, acceptability will require assurances of quality, consistency, and standardization. We are proposing a foundational framework that the industry can use to identify and validate services to better support the gathering of quality data and to better serve the PV professional.

Application of Augmented Intelligence for Pharmacovigilance Case Seriousness Determination

“…Further detail can be found in Fig. 3, which details the AQL process for PV neural networks and has been adapted with permission from its original source [45].…”

Automation in pharmaceutical sector by implementation of artificial intelligence platform: a way forward

“…14 As individual case safety report volumes increase, case processing activities constitute a significant portion of internal PV resource use. 1,7 So, additional expenditures related to contract out are considered, case processing spending, consumes most of a pharmaceutical industry overall PV budget. Automation of AE case processing through AI signifies an opportunity to affect the strongest PV cost driver.…”

“…MI is a technology 'learn' from patterns they identify or ability to understand without openly being programmed and correct behavior appropriately. 7 and/or non-imaging patient data and makes assessment of the patient's medical condition, which can be helpful to assist clinicians in their decision-making process. 8 Natural language processing (NLP) is a technology will analyze, recognize, and produce language that humans use naturally.…”

“…At many decision steps in PV process, cognitive services are identified and developed to support pharmacovigilance users. 7 Automation of jobs and AI have generated discussion across all industries that are either affected by or considering developing this technology to aid in their extensive functions. One of the mostly discussed questions is whether AI will replace humans in their roles.…”

Artificial intelligence in managing clinical trial design and conduct

“…Cognitive services, a combination of ML and NLP algorithms, have been identified and developed to solve specific tasks in the PV process of Individual Case Safety Reports, which would require human intelligence. [ 17 ] Such AI techniques can reduce the cognitive burden of PV professionals and improve efficiencies of various PV processes.…”