dc.description.abstract | Electronic health records (EHRs) are increasingly becoming common in healthcare delivery
settings, enabling physicians to continuously document patients’ care episodes in EHRs and
therefore creating digital health records of individual patients. This along with general
advances in software and computer hardware has made it increasingly easy to capture and
store patient clinical data electronically which has, in turn, yielded an increasing supply of
readily available patients’ clinical information in electronic health records. However,
retrieving clinical information from long clinical narrative texts is a challenging problem.
Clinical narrative texts provide significant difficulties for conventional natural language
processing techniques when it comes to retrieving information from long clinical text
documents. Additionally, a detailed assessment of past studies revealed a lack of theoretical
support for the computational techniques employed, and a scarcity of research on information
retrieval techniques applicable to clinical narrative texts. Most clinical information is in
narrative text form which limits users from quickly finding desired information given the
large volume of texts that needs to be read. A physician's capacity to read and get information
from text for clinical overview is severely affected when records get longer. Thus, medical
practitioners are increasingly confronted with information flood which provides more
information than practitioners can process especially in time constraint healthcare delivery
settings. Physicians still use a standard linear text layout in computer screens which one must
read through the same way one would their paper counterparts. Thus, the challenges
experienced by physicians in navigating, retrieving, and synthesizing paper-based patient
records remain unsolved with current electronic health systems. This makes the process of
identifying the most critical and significant nuggets of clinical information in a given patient
clinical record challenging but worthwhile task. Due to the continuous growth of clinical
data, automated retrieval of important pieces of information from clinical narrative text is
becoming an important research problem.
In this thesis, we proposed text classification and visualization model to support information
retrieval from clinical narrative texts in electronic health records. The objective of this study
is to overcome the challenges encountered in retrieving information from clinical narrative
texts. In our approach clinical narrative texts are classified into different class orientations
and then visualized as a cluster map. A set of five information facets were identified to
characterize relevant facets of information. We developed a deep learning algorithm to
classify clinical narrative texts into a predefined set of classes. From the state of art, it was
found out that training deep learning models is difficult, and getting them to converge in a
reasonable amount of time is still challenging. To overcome this problem, we proposed a
novel layer normalization technique called range normalization which outperformed
conventional techniques. An artefact was developed and user study was conducted to collect
feedback from healthcare practitioners on the usefulness of such a tool in supporting
information retrieval from clinical texts. The results showed that such a model would be
useful to physicians in reviewing clinical narrative texts especially for patients with lengthy
medical histories. The results from the study show that the artefact is useful and effective in
supporting physicians during care episodes. The results suggest that integrating visualization
into an electronic health record would be beneficial to physicians in satisfying their
information needs. Modelling texts into meaningful information facets provides organized
clinical narrative text documents. | en_US |