Artificial intelligence in pharmacovigilance: Do we need explainability?

Safeguarding Patients in the AI Era: Ethics at the Forefront of Pharmacovigilance

Artificial intelligence is increasingly being used in pharmacovigilance. However, the use of artificial intelligence in pharmacovigilance raises ethical concerns related to fairness, non-discrimination, compliance, and responsibility as the central ethical principles in risk assessment and regulatory requirements. This paper explores these concerns and provides a roadmap to how to address these challenges by considering data collection, privacy protection, transparency and accountability, model training, and explainability in artificial intelligence decision making for drug safety surveillance. A number of responsible approaches have been identified including an ethics framework and best practices to enhance artificial intelligence use in healthcare. The document also recognizes some initiatives that have demonstrated the importance of ethics in artificial intelligence pharmacovigilance. Nevertheless, the major needs mentioned in this paper are transparency, accountability, data protection, and fairness, which stress the necessity of collaboration to construct a cognitive framework aimed at integrating ethical artificial intelligence into pharmacovigilance. In conclusion, innovation should be balanced with ethical responsibility to enhance public health outcomes as well as patient safety.

Knowledge Graphs in Pharmacovigilance: A Step-By-Step Guide

PURPOSE

This work aims to demystify Knowledge Graphs (KGs) in pharmacovigilance (PV). It complements the scoping review within this issue. By bridging knowledge gaps and stimulating interest, further engagement with this topic by pharmacovigilance professionals will be facilitated.

METHODS

We elucidate fundamental KGs concepts and terminology, followed by delineating a sequence of implementation steps: use case definition, data type selection, data sourcing, KG construction, KG embedding, and deriving actionable insights. Information technology options and limitations are also explored.

FINDINGS

KGs in pharmacovigilance is a multi-disciplinary field involving information technology, machine learning, biology, and PV. We were able to synthesize the relevant core concepts to create an intuitive exposition of KGs in PV.

IMPLICATIONS

This work demystifies KGs with a pharmacovigilance focus, preparing readers for the accompanying in-depth scoping review. that follows. It lays the groundwork for advancing PV research and practice by emphasizing the importance of engaging with vigilance experts. This approach enhances knowledge sharing and collaboration, contributing to more effective and informed pharmacovigilance efforts and optimal assessment and deployment of KGs in PV.

Predicting falls-related admissions in older adults in Alberta, Canada: a machine-learning falls prevention tool developed using population administrative health data

OBJECTIVE

To construct a machine-learning (ML) model for health systems with organised falls prevention programmes to identify older adults at risk for fall-related admissions.

DESIGN

This prognostic study used population-level administrative health data to develop an ML prediction model.

SETTING

This study took place in Alberta, Canada during 2018-2019.

PARTICIPANTS

Albertans aged 65 and older with at least one prior admission. Those with palliative conditions or emigrated out of Alberta were excluded.

EXPOSURE

Unit of analysis was the individual person.

MAIN OUTCOMES/MEASURES

We identified fall-related admissions. A CatBoost model was developed on 2018 data to predict risk of fall-related emergency department visits or hospitalisations. Temporal validation was done using 2019 data to evaluate model performance. We reported discrimination, calibration and other relevant metrics measured at the end of 2019 on both ranked predictions and predicted probability thresholds. A cost-savings simulation was performed using 2019 data.

RESULTS

Final number of study participants was 224 445. The validation set had 203 584 participants with 19 389 fall-related events (9.5% pretest probability) and an ML model c-statistic of 0.70. The highest ranked predictions had post-test probabilities ranging from 40% to 50%. Net benefit analysis presented mixed results with some net benefit using the ML model in the 6%-30% range. The top 50 percentile of predicted risks represented nearly $C60 million in health system costs related to falls. Intervening on the top 25 or 50 percentiles of predicted risk could realise substantial (up to $C16 million) savings.

CONCLUSION

ML prediction models based on population-level administrative data can assist health systems with fall prevention programmes identify older adults at risk of fall-related admissions and reduce costs. ML predictions based on ranked predictions or probability thresholds could guide subsequent interventions to mitigate fall risks. Increased access to diverse forms of data could improve ML performance and further reduce costs.

Will the future of pharmacovigilance be more automated?

INTRODUCTION

Artificial intelligence (AI) based tools offer new opportunities for pharmacovigilance (PV) activities. Nevertheless, their contribution to PV needs to be tailored to preserve and strengthen medical and pharmacological expertise in drug safety.

AREAS COVERED

This work aims to describe PV tasks in which the contribution of AI and intelligent automation (IA) tools is required, in the context of a continuous increase of spontaneous reporting cases and regulatory tasks. A narrative review with expert selection of pertinent references was performed through Medline. Two areas were covered, management of spontaneous reporting cases and signal detection.

PERSPECTIVE

The use of AI and IA tools will assist a large spectrum of PV activities, both in public and private PV systems, in particular for tasks of low added value (e.g. initial quality check, verification of essential regulatory information, search for duplicates). Testing, validating, and integrating these tools in the PV routine are the actual challenges for modern PV systems, to guarantee high-quality standards in terms of case management and signal detection.