Towards similarity-based differential diagnostics for common diseases.

Slater, Luke T, Karwath, Andreas, Williams, John A, phenotypes, Sophie, Makepeace, Silver, Carberry, Alexander, Hoehndorf, Robert and Gkoutos, Georgios V (2021) Towards similarity-based differential diagnostics for common diseases. Computers in biology and medicine, 133. p. 104360. ISSN 1879-0534. This article is available to all UHB staff and students via ASK Discovery tool http://tinyurl.com/z795c8c by using their UHB Athens login IDs

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Abstract

Ontology-based phenotype profiles have been utilised for the purpose of differential diagnosis of rare genetic diseases, and for decision support in specific disease domains. Particularly, semantic similarity facilitates diagnostic hypothesis generation through comparison with disease phenotype profiles. However, the approach has not been applied for differential diagnosis of common diseases, or generalised clinical diagnostics from uncurated text-derived phenotypes. In this work, we describe the development of an approach for deriving patient phenotype profiles from clinical narrative text, and apply this to text associated with MIMIC-III patient visits. We then explore the use of semantic similarity with those text-derived phenotypes to classify primary patient diagnosis, comparing the use of patient-patient similarity and patient-disease similarity using phenotype-disease profiles previously mined from literature. We also consider a combined approach, in which literature-derived phenotypes are extended with the content of text-derived phenotypes we mined from 500 patients. The results reveal a powerful approach, showing that in one setting, uncurated text phenotypes can be used for differential diagnosis of common diseases, making use of information both inside and outside the setting. While the methods themselves should be explored for further optimisation, they could be applied to a variety of clinical tasks, such as differential diagnosis, cohort discovery, document and text classification, and outcome prediction.

Item Type: Article
Additional Information: This article is available to all UHB staff and students via ASK Discovery tool http://tinyurl.com/z795c8c by using their UHB Athens login IDs
Subjects: QA Mathematics. Computing
QW Microbiology. Immunology
QZ Pathology. Oncology
W Public health. Health statistics. Occupational health. Health education
Divisions: Ambulatory Care
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Depositing User: Jamie Edgar
Date Deposited: 21 Apr 2021 11:55
Last Modified: 21 Apr 2021 11:55
URI: http://www.repository.uhblibrary.co.uk/id/eprint/4229

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