ItemConference Paper

Statistical Learning inPharmacovigilance: A Data-Driven Approach to AI-Enhanced Drug Safety Monitoring

Title
Statistical Learning inPharmacovigilance: A Data-Driven Approach to AI-Enhanced Drug Safety Monitoring
Creator
John Joshua, H.; Prathish, R.K.; Vasanth, U.; Jaya Priya, J.; Vinay, M.; Deepa, S.
Description
Pharmacovigilance is transforming at warp speed in response to big data and advanced analytical techniques. This paper will provide an overview of where pharmacovigilance currently stands by focusing on integrating artificial intelligence (AI), machine learning (ML) and real-world data (RWD) in order to improve drug safety monitoring. These new methods are increasingly supplementing traditional ones which serve as their base. The purpose of this survey is to assess how effective they are, point out the major challenges standing in their way as well as offer recommendations for future research. In conclusion, although AI and ML could prove helpful especially with handling large volume and complexity of datasets, there is a need for tackling data quality, integration issues and regulatory acceptance concerns first. Standardized methodologies should be worked out and collaboration among all stakeholders encouraged so as to maximize the pharmacovigilance benefits that can come from these technologies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
Source
Lecture Notes in Networks and Systems;Volume;1612 LNNS;pp.155-166
Date
01-01-2026
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Adverse drug reactions (ADRs); Data mining; Pharmacovigilance; Real-world data (RWD)
Coverage
John Joshua H., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India; Prathish R.K., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India; Vasanth U., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India; Jaya Priya J., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India; Vinay M., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India; Deepa S., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-981952871-4;
Format
online
Language
English
Type
Conference paper

Citation

John Joshua, H.; Prathish, R.K.; Vasanth, U.; Jaya Priya, J.; Vinay, M.; Deepa, S., “Statistical Learning inPharmacovigilance: A Data-Driven Approach to AI-Enhanced Drug Safety Monitoring,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25428.

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