Neural network methods for diagnosing patient conditions from cardiopulmonary exercise testing data
Abstract
Cardiopulmonary exercise testing (CPET) provides a reliable and reproducible approach to measuring fitness in patients and diagnosing their health problems. However, the data from CPET consist of multiple time series that require training to interpret. Part of this training teaches the use of flow charts or nested decision trees to interpret the CPET results. This paper investigates the use of two machine learning techniques using neural networks to predict patient health conditions with CPET data in contrast to flow charts. The data for this investigation comes from a small sample of patients with known health problems and who had CPET results. The small size of the sample data also allows us to investigate the use and performance of deep learning neural networks on health care problems with limited amounts of labeled training and testing data.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 13, 2022
- Source ID
- 10.1186/s13040-022-00299-6
Entities
People
- Arthur Weltman
- Donald E. Brown
- James A. Jablonski
- Suchetha Sharma
Organizations
- National Center for Advancing Translational Sciences
- Naval Postgraduate School