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Text‐based phenotyping with Semantic Deep Learning: setting up experiments for women's health

Lookup NU author(s): Dr Phillip Lord



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).Semantic Deep Learning (SemDeep) aims to combine Semantic Web and Deep Learning research. Vector arithmetic formulas can be applied to neural language models from Deep Learning. We set up experiments to investigate if incorporating prior knowledge (what is known about the disease) into the vector arithmetic formulas may bring a better performance. This paper investigates a SemDeep approach for text-based phenotyping of four health issues affecting women worldwide: menopause, endometriosis, miscarriage, and infertility. The candidates for the disease phenotype are n-grams that can be mapped to SNOMED CT.

Publication metadata

Author(s): Arguello Casteleiro M, Joyce N, Maroto N, Fernandez Prieto MJ, Furmston T, Maseda Fernandez D, Lord P, Wroe C, Keane J, Cheong Y, Stevens R

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 14th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences 2023 (SWAT4HCLS 2023)

Year of Conference: 2023

Pages: 161-162

Online publication date: 22/06/2023

Acceptance date: 02/04/2022

Date deposited: 03/10/2023

ISSN: 1613-0073

Publisher: CEUR-WS


Series Title: CEUR Workshop Proceedings