3 research outputs found
From Free Text to Clusters of Content in Health Records: An Unsupervised Graph Partitioning Approach
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.Comment: 25 pages, 2 tables, 8 figures and 5 supplementary figure