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Recognizing diseases from physiological time series data using probabilistic model

Journal Article


Abstract


  • © 2018, Springer Nature Switzerland AG. 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.

UOW Authors


  •   Wang, Danni (external author)
  •   Liu, Li (external author)
  •   Guoxin Su
  •   Li, Yande (external author)
  •   Khan, Aamir (external author)

Publication Date


  • 2018

Citation


  • Wang, D., Liu, L., Su, G., Li, Y. & Khan, A. (2018). Recognizing diseases from physiological time series data using probabilistic model. Lecture Notes in Artificial Intelligence, 11061 388-399. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Scopus Eid


  • 2-s2.0-85052227575

Number Of Pages


  • 11

Start Page


  • 388

End Page


  • 399

Volume


  • 11061

Place Of Publication


  • Germany

Abstract


  • © 2018, Springer Nature Switzerland AG. 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.

UOW Authors


  •   Wang, Danni (external author)
  •   Liu, Li (external author)
  •   Guoxin Su
  •   Li, Yande (external author)
  •   Khan, Aamir (external author)

Publication Date


  • 2018

Citation


  • Wang, D., Liu, L., Su, G., Li, Y. & Khan, A. (2018). Recognizing diseases from physiological time series data using probabilistic model. Lecture Notes in Artificial Intelligence, 11061 388-399. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Scopus Eid


  • 2-s2.0-85052227575

Number Of Pages


  • 11

Start Page


  • 388

End Page


  • 399

Volume


  • 11061

Place Of Publication


  • Germany