A natural
language processing tool was able to identify diabetic patients at high risk of
low blood sugar.
By Jessica
Kent
July 29,
2019 - Using natural language processing tools could help providers
better detect low blood sugar in patients with diabetes, leading to improved
chronic disease management, revealed a study published in the journal Current
Medical Research and Opinion.
Low blood sugar, or
hypoglycemia, occurs in 20 percent to 60 percent of patients with diabetes,
researchers said. Many individuals with diabetes are unaware when hypoglycemia
occurs, especially if they have recurring episodes of low blood sugar.
Intervening and
preventing hypoglycemia is critical for patients, as the condition can lead to
adverse events like cognitive impairment, coma, and even death.
Researchers from
Regenstrief Institute, Indiana University School of Medicine, and Merck
gathered EHR data across ten years covering nearly 39,000 patients with
diabetes. The team used laboratory tests, diagnostic codes, and natural language processing (NLP)
technology to identify episodes of hypoglycemia among individuals with
diabetes.
The group found that
NLP was a viable way to identify hypoglycemia, because there weren’t always lab
tests to confirm the episode. Often, hypoglycemic episodes were only recorded
in narrative clinical notes.
Researchers also
found that the strongest predictors of hypoglycemia are recent infections,
using insulin other than long-acting insulin, recent occurrences of
hypoglycemia, and dementia. Variables associated with the lowest risk of low
blood sugar were using long-acting insulin in combination with other drugs, as
well as being 75 years of age or older.
“Knowledge of these
factors could assist clinicians in identifying patients with higher risk of
hypoglycemia, allowing them to intervene to help their patients in lowering
that risk,” said Michael Weiner, MD, MPH, director of the Regenstrief Institute
William M. Tierney Center for Health Services Research and the senior author of
the study.
“Some factors
influencing hypoglycemia may not be immediately obvious. In addition,
reassessing hypoglycemia risk as a patient's health status changes may be
important as new factors are identified.”
NLP tools have
demonstrated their value in a wide range of areas in healthcare. The technology
has previously shown its ability to detect social determinant search
terms in the EHRs of high-risk patients, to help patients better understand the data in their
EHRs, and to accurately record medical
conversations between patients and providers.
Market analysts have
also noted the strong impact that NLP and other artificial intelligence
technologies are expected to make on clinical decision support tools. In 2017,
a series of market reports discussed
the growing demand for clinical decision support tools that feed on
unstructured data, and the important role NLP will play in advancing these
tools.
“Used as a part of
artificial intelligence systems, applications of NLP technologies are being
deployed for predictive analysis and clinical decision support systems,” one
report said. “The global healthcare natural language processing market is
expected to receive an impetus from the uptake of these technologies by several
companies for extracting knowledge from several clinic documents via machine
learning or deep learning applications.”
“The growing volume
of unstructured clinical data and the unstoppable penetration of EHR systems
are expected to fuel the growth of this market in the coming years.”
The researchers on
the hypoglycemia study said they will examine the development of a clinical decision support tool that
examines EHR data to tell clinicians when their patients have hypoglycemic risk
factors.
The team will conduct
an outpatient study that uses wearable devices to monitor and record the
actions and continuous glucose levels of people with diabetes.
The information
collected includes physical activity, diet and adherence to medication
regimens, all of which is not typically included in medical records. The goal
is to identify patterns that enable providers to detect hypoglycemia
earlier.
“This study has
implications for clinical support,” said Weiner. “The predictive model could
lead to changes in practice as well as new strategies to help patients lower
their risk of hypoglycemia.”
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