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Recognizing Diseases from Physiological Time Series Data Using Probabilistic Model

  • Danni Wang
  • Li Liu
  • Guoxin Su
  • Yande Li
  • Aamir Khan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11061)

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.

Keywords

Disease pattern recognition Physiological signals Atomic pattern Temporal relationship 

Notes

Acknowledgements

This work was supported by grants from the Fundamental Research Funds for the Key Research Programm of Chongqing Science & Technology Commission (grant no. cstc2017rgzn-zdyf0064), the Chongqing Provincial Human Resource and Social Security Department (grant no. cx2017092), the Central Universities in China (grant nos. CQU0225001104447).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Big Data and Software EngineeringChongqing UniversityChongqingPeople’s Republic of China
  2. 2.School of Computing and Information TechnologyUniversity of WollongongWollongongAustralia
  3. 3.College of Information Science and EngineeringLanzhou UniversityLanzhouPeople’s Republic of China

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