A cyber-physical system framework for early detection of paroxysmal diseases

Zuxing Gu, Yu Jiang, Min Zhou, Ming Gu, Xiaoyu Song, Lui Sha

Research output: Contribution to journalArticle

Abstract

Paroxysmal diseases of inpatients are globally recognized as one of the top challenges in medicine. Poor clinical outcomes are primarily caused by delayed recognition, especially due to diverse clinical diagnostic criteria with complex manifestations, irregular episodes, and already overloaded clinical activities. With the proliferation of measuring devices and increased computational capabilities, cyber-physical characterization plays an increasingly important role in many domains to provide enabling technologies. This paper presents a cyber-physical system (CPS) framework to assist physicians in making earlier diagnoses of paroxysmal sympathetic hyperactivity based on existing medical knowledge. We propose a configurable diagnostic knowledge model to characterize clinical criteria to reduce domain knowledge deficiency between physicians and computer scientists. We present a component-based medical CPS framework to employ the knowledge models and integrate medical devices. Our approach aims to relieve medical staff from the heavy burden of clinical activities and to provide timely decision support. We evaluate our approach on 128 real-world clinical cases. Compared with the state-of-the-art approach, the results demonstrate that we enable early detection in 11.02% more patients and detect the condition 16.57 hours earlier on average.

Original languageEnglish (US)
Pages (from-to)34834-34845
Number of pages12
JournalIEEE Access
Volume6
DOIs
StatePublished - Jun 23 2018

Fingerprint

Medicine
Cyber Physical System

Keywords

  • Cyber-physical system
  • early detection
  • knowledge model
  • paroxysmal disease

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

Cite this

A cyber-physical system framework for early detection of paroxysmal diseases. / Gu, Zuxing; Jiang, Yu; Zhou, Min; Gu, Ming; Song, Xiaoyu; Sha, Lui.

In: IEEE Access, Vol. 6, 23.06.2018, p. 34834-34845.

Research output: Contribution to journalArticle

Gu, Zuxing ; Jiang, Yu ; Zhou, Min ; Gu, Ming ; Song, Xiaoyu ; Sha, Lui. / A cyber-physical system framework for early detection of paroxysmal diseases. In: IEEE Access. 2018 ; Vol. 6. pp. 34834-34845.
@article{34a08623c9b9459597f81b1cd9cea67b,
title = "A cyber-physical system framework for early detection of paroxysmal diseases",
abstract = "Paroxysmal diseases of inpatients are globally recognized as one of the top challenges in medicine. Poor clinical outcomes are primarily caused by delayed recognition, especially due to diverse clinical diagnostic criteria with complex manifestations, irregular episodes, and already overloaded clinical activities. With the proliferation of measuring devices and increased computational capabilities, cyber-physical characterization plays an increasingly important role in many domains to provide enabling technologies. This paper presents a cyber-physical system (CPS) framework to assist physicians in making earlier diagnoses of paroxysmal sympathetic hyperactivity based on existing medical knowledge. We propose a configurable diagnostic knowledge model to characterize clinical criteria to reduce domain knowledge deficiency between physicians and computer scientists. We present a component-based medical CPS framework to employ the knowledge models and integrate medical devices. Our approach aims to relieve medical staff from the heavy burden of clinical activities and to provide timely decision support. We evaluate our approach on 128 real-world clinical cases. Compared with the state-of-the-art approach, the results demonstrate that we enable early detection in 11.02{\%} more patients and detect the condition 16.57 hours earlier on average.",
keywords = "Cyber-physical system, early detection, knowledge model, paroxysmal disease",
author = "Zuxing Gu and Yu Jiang and Min Zhou and Ming Gu and Xiaoyu Song and Lui Sha",
year = "2018",
month = "6",
day = "23",
doi = "10.1109/ACCESS.2018.2850039",
language = "English (US)",
volume = "6",
pages = "34834--34845",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - A cyber-physical system framework for early detection of paroxysmal diseases

AU - Gu, Zuxing

AU - Jiang, Yu

AU - Zhou, Min

AU - Gu, Ming

AU - Song, Xiaoyu

AU - Sha, Lui

PY - 2018/6/23

Y1 - 2018/6/23

N2 - Paroxysmal diseases of inpatients are globally recognized as one of the top challenges in medicine. Poor clinical outcomes are primarily caused by delayed recognition, especially due to diverse clinical diagnostic criteria with complex manifestations, irregular episodes, and already overloaded clinical activities. With the proliferation of measuring devices and increased computational capabilities, cyber-physical characterization plays an increasingly important role in many domains to provide enabling technologies. This paper presents a cyber-physical system (CPS) framework to assist physicians in making earlier diagnoses of paroxysmal sympathetic hyperactivity based on existing medical knowledge. We propose a configurable diagnostic knowledge model to characterize clinical criteria to reduce domain knowledge deficiency between physicians and computer scientists. We present a component-based medical CPS framework to employ the knowledge models and integrate medical devices. Our approach aims to relieve medical staff from the heavy burden of clinical activities and to provide timely decision support. We evaluate our approach on 128 real-world clinical cases. Compared with the state-of-the-art approach, the results demonstrate that we enable early detection in 11.02% more patients and detect the condition 16.57 hours earlier on average.

AB - Paroxysmal diseases of inpatients are globally recognized as one of the top challenges in medicine. Poor clinical outcomes are primarily caused by delayed recognition, especially due to diverse clinical diagnostic criteria with complex manifestations, irregular episodes, and already overloaded clinical activities. With the proliferation of measuring devices and increased computational capabilities, cyber-physical characterization plays an increasingly important role in many domains to provide enabling technologies. This paper presents a cyber-physical system (CPS) framework to assist physicians in making earlier diagnoses of paroxysmal sympathetic hyperactivity based on existing medical knowledge. We propose a configurable diagnostic knowledge model to characterize clinical criteria to reduce domain knowledge deficiency between physicians and computer scientists. We present a component-based medical CPS framework to employ the knowledge models and integrate medical devices. Our approach aims to relieve medical staff from the heavy burden of clinical activities and to provide timely decision support. We evaluate our approach on 128 real-world clinical cases. Compared with the state-of-the-art approach, the results demonstrate that we enable early detection in 11.02% more patients and detect the condition 16.57 hours earlier on average.

KW - Cyber-physical system

KW - early detection

KW - knowledge model

KW - paroxysmal disease

UR - http://www.scopus.com/inward/record.url?scp=85049064203&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049064203&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2018.2850039

DO - 10.1109/ACCESS.2018.2850039

M3 - Article

AN - SCOPUS:85049064203

VL - 6

SP - 34834

EP - 34845

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -