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

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. Electronic supplementary material The online version of this article (10.1007/s41109-018-0109-9) contains supplementary material, which is available to authorized users.

Does the Integration of Pre-Coded Information with Narratives Improve in-Hospital Falls’ Surveillance?

“…To our knowledge, no previous studies have assessed if the integration of information from narratives improves the understanding of falls in the hospital setting; however, similar studies have been conducted in other settings with promising results [12,29]. Results of the present study did not demonstrate the hypothesis that the integration of the information improves the understanding of in-hospital falls since the cluster analysis did not improve by adding information derived from narratives.…”

Graph-based data clustering via multiscale community detection

“…Importantly, graph-based clustering is also able to reveal modular structure in graphs across levels of resolution through multiscale community detection [15,16,17]. This approach allows for the discovery of natural data clusterings of different coarseness [18], thus recasting the problem of finding the appropriate number of clusters to the detection of relevant scales in the graph.…”

Social Media Analytics of the Internet of Things

“…However, the leading trends identi ed did not vary between four and ve clusters as illustrated in the word clouds below. Word clouds are basic and intuitive tools that allow us to evaluate text results for insight [30].…”