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Sentence Compression with Reinforcement Learning Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Recognizing Diseases from Physiological Time Series Data Using Probabilistic Model

Authors : Danni Wang, Li Liu, Guoxin Su, Yande Li, Aamir Khan

Published in: Knowledge Science, Engineering and Management

Publisher: Springer International Publishing

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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.

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Metadata
Title
Recognizing Diseases from Physiological Time Series Data Using Probabilistic Model
Authors
Danni Wang
Li Liu
Guoxin Su
Yande Li
Aamir Khan
Copyright Year
2018
Book
Knowledge Science, Engineering and Management

Print ISBN: 978-3-319-99364-5

Electronic ISBN: 978-3-319-99365-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-99365-2_34

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