Abstract
Modern clinical databases collect a large amount of time series data of vital signs. In this work, we first extract the general representative signal patterns from physiological signals, such as blood pressure, respiration rate and heart rate, referred to as atomic patterns. By assuming the same disease may share the same styles of atomic patterns and their temporal dependencies, we present a probabilistic framework to recognize diseases from physiological data in the presence of uncertainty. To handle the temporal relationships among atomic patterns, Allen’s interval relations and latent variables originated from Chinese restaurant process are utilized to characterize the unique sets of interval configurations of a disease. We evaluate the proposed framework using MIMIC-III database, and the experimental results show that our approach outperforms other competitive models.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.