Artificial intelligence methods for a Bayesian epistemology-powered evidence evaluation.

Affiliation

De Pretis F(1)(2), Landes J(3), Peden W(4)(5).
Author information:
(1)Department of Biomedical Sciences and Public Health, School of Medicine and Surgery, Marche Polytechnic University, Ancona, Italy.
(2)Department of Communication and Economics, University of Modena and Reggio Emilia, Reggio Emilia, Italy.
(3)Munich Center for Mathematical Philosophy, Faculty of Philosophy, Philosophy of Science and Study of Religion, Ludwig-Maximilians-Universität München, Munich, Germany.
(4)Erasmus Institute for Philosophy and Economics, Erasmus School of Philosophy, Erasmus University Rotterdam, Rotterdam, The Netherlands.
(5)Department of Philosophy, Durham University, Durham, UK.

Abstract

RATIONALE, AIMS AND OBJECTIVES: The diversity of types of evidence (eg, case reports, animal studies and observational studies) makes the assessment of a drug's safety profile into a formidable challenge. While frequentist uncertain inference struggles in aggregating these signals, the more flexible Bayesian approaches seem better suited for this quest. Artificial Intelligence (AI) offers great promise to these approaches for information retrieval, decision support, and learning probabilities from data. METHODS: E-Synthesis is a Bayesian framework for drug safety assessments built on philosophical principles and considerations. It aims to aggregate all the available information, in order to provide a Bayesian probability of a drug causing an adverse reaction. AI systems are being developed for evidence aggregation in medicine, which increasingly are automated. RESULTS: We find that AI can help E-Synthesis with information retrieval, usability (graphical decision-making aids), learning Bayes factors from historical data, assessing quality of information and determining conditional probabilities for the so-called 'indicators' of causation for E-Synthesis. Vice versa, E-Synthesis offers a solid methodological basis for (semi-)automated evidence aggregation with AI systems. CONCLUSIONS: Properly applied, AI can help the transition of philosophical principles and considerations concerning evidence aggregation for drug safety to a tool that can be used in practice.