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Artificial intelligence methods for a Bayesian epistemology‐powered evidence evaluation
Author(s) -
De Pretis Francesco,
Landes Jürgen,
Peden William
Publication year - 2021
Publication title -
journal of evaluation in clinical practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.737
H-Index - 73
eISSN - 1365-2753
pISSN - 1356-1294
DOI - 10.1111/jep.13542
Subject(s) - computer science , frequentist inference , bayesian probability , artificial intelligence , bayes' theorem , machine learning , bayesian network , bayesian inference , data science
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.