Towards Similarity-based Differential Diagnostics For Common Diseases

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.

Citation
Slater, L. T., Karwath, A., Williams, J. A., Russell, S., Makepeace, S., Carberry, A., … Gkoutos, G. V. (2021). Towards Similarity-based Differential Diagnostics For Common Diseases. Computers in Biology and Medicine, 104360. doi:10.1016/j.compbiomed.2021.104360

Acknowledgements
GVG and LTS acknowledge support from support from the NIHR Birmingham ECMC, NIHR Birmingham SRMRC, Nanocommons H2020-EU (731032) and the NIHR Birmingham Biomedical Research Centre and the MRC HDR UK (HDRUK/CFC/01), an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Medical Research Council or the Department of Health. RH and GVG were supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3790-01-01. AK was supported by by the Medical Research Council (MR/S003991/1) and the MRC HDR UK (HDRUK/CFC/01).

Publisher
Elsevier BV

Journal
Computers in Biology and Medicine

DOI
10.1016/j.compbiomed.2021.104360

Additional Links
https://linkinghub.elsevier.com/retrieve/pii/S0010482521001542

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2021-04-04 08:19:40
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