BibTeX

@article{kokkinakis-2011-natural-149930,
	title        = {Natural language processing of clinical data with a focus on diffuse symptoms},
	abstract     = {The medical domain is well supported with a wealth of large, rich and
varied controlled vocabularies and terminological resources. This paper
investigates the extent by which the largest available medical nomenclature
for Swedish, the Systematized Nomenclature of Medicine Clinical Terms
(SNOMED CT), can handle a particularly challenging and difficult to
automatically acquire type of terminology, namely (clinical) phenotypes.
The aim of the study is to better understand phenotype contextualization in
order to improve and enhance our knowledge of communicative events in
various healthcare settings. Our approach can be seen as an exploratory one
in which we believe to yield useful insights into the nature of how findings,
symptoms and signs (i.e. clinical phenotypes in general) are expressed in
real data. This study is initiated in the context of the project "Interpretation
and understanding of functional symptoms in primary health care". The
main research goal of which is to study health care interactions with
patients suffering from Functional Somatic Syndromes (FSS). FSS are
characterized by particular constellations of medically unexplained, often
chronic symptoms, such as dizziness, fatigue, dyspepsia, muscle and joint
pain.
We use methods from the natural language processing field in order to
investigate how symptom mentions are expressed and how available
successful automated means are for capturing symptom descriptions both
on collected written (patient records) and transcribed material
(patient/nurse and patient/doctor encounters).
We manually evaluated the content of the resource on the collected data
and our results indicate that a large number of such phenotypes are
expressed using figurative language, or contextualized using a number of
variant expressions. SNOMED CT cannot easily accommodate for such
variation and vagueness expressed in real text data, unless we devise means
to handle such variation, e.g. by the use of near synonym dictionaries,
development and linking of consumer health vocabularies. The presented
research has several implications since accurate identification of
phenotypes can for instance increase the value of available data in decision
making and thus allow automatic systems to dynamically correct
inappropriate domain decisions.
We have evaluated the content of a large controlled vocabulary for Swedish
on symptom descriptions in clinical texts.},
	journal      = {Läkaresällskapets Riksstämman },
	author       = {Kokkinakis, Dimitrios},
	year         = {2011},
}