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PreprintCited by 27History of Medicine StudiesEthnobotanical and Medicinal Plants StudiesHistory of Science and Medicine

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

Open access
Connelly, Erin, del Genio, Charo I., Harrison, Freya·2018·mBio
DOI 10.1101/368779PMC PMC7018648

Preprint — published as 10.1128/mbio.03136-19 · Paper page

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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.

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FIG 1 Community co-occurrence of ingredients. Every pixel in the figure corresponds to a pair of ingredients. The saturation of the color is proportional to the number of times that the pair of ingredients is found in the same community, according to the scale on the right-hand border of the figure.
FIG 1 Community co-occurrence of ingredients. Every pixel in the figure corresponds to a pair of ingredients. The saturation of the color is proportional to the number of times that the pair of ingredients is found in the same community, according to the scale on the right-hand border of the figure.CC BY · PubMed Central
FIG 2 Example of an ingredient network. The nodes in yellow are ingredients of a recipe for the treatment of fistula in lacrimali; those in blue are ingredients of a recipe for the treatment of pascionibus oris. Ingredients found in both recipes are colored both yellow and blue. Thicker links join pairs of ingredients that appear in both recipes.
FIG 2 Example of an ingredient network. The nodes in yellow are ingredients of a recipe for the treatment of fistula in lacrimali; those in blue are ingredients of a recipe for the treatment of pascionibus oris. Ingredients found in both recipes are colored both yellow and blue. Thicker links join pairs of ingredients that appear in both recipes.CC BY · PubMed Central
FIG 3 Fictitious example of the process for identifying relevant combinations of ingredients via thresholding and community detection. (A) The starting network. (B, C) Apply the thresholding procedure and choose the partitioned network with maximal modularity (q{c}, equation 2). (D) Draw a heat map to visualize the strengths of associations between ingredient pairs. Darker pixels indicate a stronger association. For example, in this example, network ingredients 1 and 3 are more strongly connected by their recipe co-occurrences than are ingredients 1 and 2 or ingredients 2 and 3. (E) Identify the most strongly associated group of ingredients and manually search the database for recipes including these combinations.
FIG 3 Fictitious example of the process for identifying relevant combinations of ingredients via thresholding and community detection. (A) The starting network. (B, C) Apply the thresholding procedure and choose the partitioned network with maximal modularity (q{c}, equation 2). (D) Draw a heat map to visualize the strengths of associations between ingredient pairs. Darker pixels indicate a stronger association. For example, in this example, network ingredients 1 and 3 are more strongly connected by their recipe co-occurrences than are ingredients 1 and 2 or ingredients 2 and 3. (E) Identify the most strongly associated group of ingredients and manually search the database for recipes including these combinations.CC BY · PubMed Central

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Metadata

Authors
Connelly, E., del Genio, C. I., & Harrison, F.
Affiliations
  • University of Pennsylvania (US)
  • University of Warwick (GB)
Corresponding author
  • Connelly, Erin
  • del Genio, Charo I.
  • Harrison, Freya
ORCID
Venue
mBio
Publisher
openRxiv
Published
2018-07-16
Volume
11
Issue
1
ISSN
2150-7511 (ISSN-L)
DOI
10.1101/368779
PMC
PMC7018648
OpenAlex
W3005612885
Cited by
27
References
1
Citations by year
2026: 3 · 2025: 5 · 2024: 5 · 2023: 3 · 2022: 4 · 2021: 2 · 2020: 4 · 2019: 1
FWCI
53.87
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • mBio (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Pure (Coventry University) (acceptedVersion, cc-by)
  • Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • University of Pennsylvania
  • University of Warwick
  • University of Nottingham
Topics
  • Social Sciences › Arts and Humanities › History › History of Medicine Studies
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Ethnobotanical and Medicinal Plants Studies
  • Social Sciences › Arts and Humanities › History and Philosophy of Science › History of Science and Medicine
Keywords
medieval medicine, antimicrobial activity, topical treatment, microbial infection, ethnopharmacology, historical medical texts, network analysis, antibiotic development
MeSH terms
Anti-Bacterial Agents, Electronic Data Processing, Communicable Disease Control, Communicable Diseases, Humans, Materia Medica, Reference Books, Medical, Ethnopharmacology, History, Medieval, Data Mining

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Connelly, E., del Genio, C. I., & Harrison, F. (2018). Datamining a medieval medical text reveals patterns in ingredient choice that reflect biological activity against the causative agents of specified infections. mBio. https://doi.org/10.1101/368779

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