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From free text to clusters of content in health records: An unsupervised graph partitioning approach

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Title: From free text to clusters of content in health records: An unsupervised graph partitioning approach
Authors: Altuncu, MT
Mayer, E
Yaliraki, SN
Barahona, M
Item Type: Journal Article
Abstract: Electronic Healthcare records contain large volumes of unstructured data in different forms. Free text constitutes a large portion of such data, yet this source of richly detailed information often remains under-used in practice because of a lack of suitable methodologies to extract interpretable content in a timely manner. Here we apply network-theoretical tools to the analysis of free text in Hospital Patient Incident reports in the English National Health Service, to find clusters of reports in an unsupervised manner and at different levels of resolution based directly on the free text descriptions contained within them. To do so, we combine recently developed deep neural network text-embedding methodologies based on paragraph vectors with multi-scale Markov Stability community detection applied to a similarity graph of documents obtained from sparsified text vector similarities. We showcase the approach with the analysis of incident reports submitted in Imperial College Healthcare NHS Trust, London. The multiscale community structure reveals levels of meaning with different resolution in the topics of the dataset, as shown by relevant descriptive terms extracted from the groups of records, as well as by comparing a posteriori against hand-coded categories assigned by healthcare personnel. Our content communities exhibit good correspondence with well-defined hand-coded categories, yet our results also provide further medical detail in certain areas as well as revealing complementary descriptors of incidents beyond the external classification. We also discuss how the method can be used to monitor reports over time and across different healthcare providers, and to detect emerging trends that fall outside of pre-existing categories.
Issue Date: 24-Jan-2019
Date of Acceptance: 5-Nov-2018
URI: http://hdl.handle.net/10044/1/66234
DOI: https://dx.doi.org/10.1007/s41109-018-0109-9
ISSN: 2364-8228
Publisher: SpringerOpen
Journal / Book Title: Applied Network Science
Volume: 4
Copyright Statement: © The Author(s). 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Imperial College Healthcare NHS Trust- BRC Funding
National Institute for Health Research
Funder's Grant Number: EP/I017267/1
EP/N014529/1
RDB04
n/a
Keywords: Graph theory
Markov Stability partition algorithm
Text embedding
Topic clustering
Unsupervised multi-resolution clustering
cs.CL
cs.CL
cs.IR
cs.LG
cs.SI
math.SP
Publication Status: Published
Article Number: ARTN 2
Appears in Collections:Division of Surgery
Mathematics
Chemistry
Biological and Biophysical Chemistry
Applied Mathematics and Mathematical Physics
Faculty of Medicine
Faculty of Natural Sciences



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