Datamining a medieval medical text reveals patterns in ingredient choice that reflect biological activity against the causative agents of specified infections

TL;DR

This study digitizes a medieval medical text and applies network analysis to uncover patterns in ingredient choices that may reflect rational biological activity against infections, offering new insights into historical ethnopharmacology.

Contribution

It demonstrates a novel approach of converting medieval medical texts into electronic databases for algorithmic analysis, revealing meaningful patterns in ingredient selection.

Findings

Identified statistically significant ingredient patterns

Patterns suggest rational medical decisions in medieval remedies

Showcased interdisciplinary potential of data-driven textual analysis

Abstract

The pharmacopeia used by physicians and lay people in medieval Europe has largely been dismissed as placebo or superstition. While we now recognise that some of the materia medica used by medieval physicians could have had useful biological properties, research in this area is limited by the labour-intensive process of searching and interpreting historical medical texts. Here, we demonstrate the potential power of turning medieval medical texts into contextualised electronic databases amenable to exploration by algorithm. We use established methodologies from network science to reveal statistically significant patterns in ingredient selection and usage in a key text, the fifteenth-century Lylye of Medicynes, focusing on remedies to treat symptoms of microbial infection. We discuss the potential that these patterns reflect rational medical decisions. In providing a worked example of data-driven textual analysis, we demonstrate the potential of this approach to encourage interdisciplinary collaboration and to shine a new light on the ethnopharmacology of historical medical texts.