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Classification of orthostatic intolerance through data analytics

Journal Article · · Medical and Biological Engineering and Computing
 [1];  [2];  [1];  [3];  [4];  [1];  [1]
  1. North Carolina State Univ., Raleigh, NC (United States)
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  3. Novo Nordisk A/S, Bagsvaerd (Denmark)
  4. Univ. of Copenhagen (Denmark)
+ Show Author Affiliations

Imbalance in the autonomic nervous system can lead to orthostatic intolerance manifested by dizziness, lightheadedness, and a sudden loss of consciousness (syncope); these are common conditions, but they are challenging to diagnose correctly. Uncertainties about the triggering mechanisms and the underlying pathophysiology have led to variations in their classification. This study uses machine learning to categorize patients with orthostatic intolerance. Here we use random forest classification trees to identify a small number of markers in blood pressure, and heart rate time-series data measured during head-up tilt to (a) distinguish patients with a single pathology and (b) examine data from patients with a mixed pathophysiology. Next, we use Kmeans to cluster the markers representing the time-series data. We apply the proposed method analyzing clinical data from 186 subjects identified as control or suffering from one of four conditions: postural orthostatic tachycardia (POTS), cardioinhibition, vasodepression, and mixed cardioinhibition and vasodepression. Classification results confirm the use of supervised machine learning. We were able to categorize more than 95% of patients with a single condition and were able to subgroup all patients with mixed cardioinhibitory and vasodepressor syncope. Clustering results confirm the disease groups and identify two distinct subgroups within the control and mixed groups. The proposed study demonstrates how to use machine learning to discover structure in blood pressure and heart rate time-series data. The methodology is used in classification of patients with orthostatic intolerance. Diagnosing orthostatic intolerance is challenging, and full characterization of the pathophysiological mechanisms remains a topic of ongoing research. This study provides a step toward leveraging machine learning to assist clinicians and researchers in addressing these challenges.

Research Organization:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA); National Institutes of Health (NIH); National Science Foundation (NSF)
Grant/Contract Number:
NA0003525
OSTI ID:
1834334
Report Number(s):
SAND2020-2937J; 684640
Journal Information:
Medical and Biological Engineering and Computing, Journal Name: Medical and Biological Engineering and Computing Journal Issue: 3 Vol. 59; ISSN 0140-0118
Publisher:
Springer NatureCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

60 APPLIED LIFE SCIENCES
classification
clustering
machine learning
orthostatic intolerance
syncope