A machine learning technique to detect and predict parkinson's disease

Peijiang Yuan; Bo Zhang; Jianmin Li; Julei Wang; Guodong Gao

A machine learning technique to detect and predict parkinson's disease

Peijiang Yuan^*
, Bo Zhang
, Jianmin Li
, Julei Wang
, Guodong Gao

^*Corresponding author for this work

Tsinghua University
Tangdu Hospital, Fourth Military Medical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Parkinson's Disease (PD) is a neurological disorder that has been a hot topic worldwide. Human neurological disorders can be modeled in animals like rats and monkeys using standardized procedures that recreate specific pathogenic events and their behavioral outcomes. Different methods have been proposed to detect and verify the efficiency and effectiveness of such models. However, the inner scheme to detect and predict PD at the early stage is still a difficult problem. In this paper, a Conditional Random Fields (CRFs) based approach for PD image detection and prediction is presented. Machine learning techniques are discussed that proved to be useful in detecting and predicting PD in animal models.

Original language	English
Title of host publication	Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009
Pages	147-152
Number of pages	6
State	Published - 2009
Externally published	Yes
Event	2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009 - Las Vegas, NV, United States Duration: 13 Jul 2009 → 16 Jul 2009

Publication series

Name	Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009
Volume	1

Conference

Conference	2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009
Country/Territory	United States
City	Las Vegas, NV
Period	13/07/09 → 16/07/09

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Cite this

Yuan, P., Zhang, B., Li, J., Wang, J., & Gao, G. (2009). A machine learning technique to detect and predict parkinson's disease. In Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009 (pp. 147-152). (Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009; Vol. 1).

Yuan, Peijiang ; Zhang, Bo ; Li, Jianmin et al. / A machine learning technique to detect and predict parkinson's disease. Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009. 2009. pp. 147-152 (Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009).

@inproceedings{c9e1057376fb4a77a8155946510d63e6,

title = "A machine learning technique to detect and predict parkinson's disease",

abstract = "Parkinson's Disease (PD) is a neurological disorder that has been a hot topic worldwide. Human neurological disorders can be modeled in animals like rats and monkeys using standardized procedures that recreate specific pathogenic events and their behavioral outcomes. Different methods have been proposed to detect and verify the efficiency and effectiveness of such models. However, the inner scheme to detect and predict PD at the early stage is still a difficult problem. In this paper, a Conditional Random Fields (CRFs) based approach for PD image detection and prediction is presented. Machine learning techniques are discussed that proved to be useful in detecting and predicting PD in animal models.",

author = "Peijiang Yuan and Bo Zhang and Jianmin Li and Julei Wang and Guodong Gao",

year = "2009",

language = "英语",

isbn = "9781601321190",

series = "Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009",

pages = "147--152",

booktitle = "Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009",

note = "2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009 ; Conference date: 13-07-2009 Through 16-07-2009",

}

Yuan, P, Zhang, B, Li, J, Wang, J & Gao, G 2009, A machine learning technique to detect and predict parkinson's disease. in Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009. Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009, vol. 1, pp. 147-152, 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009, Las Vegas, NV, United States, 13/07/09.

A machine learning technique to detect and predict parkinson's disease. / Yuan, Peijiang; Zhang, Bo; Li, Jianmin et al.
Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009. 2009. p. 147-152 (Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A machine learning technique to detect and predict parkinson's disease

AU - Yuan, Peijiang

AU - Zhang, Bo

AU - Li, Jianmin

AU - Wang, Julei

AU - Gao, Guodong

PY - 2009

Y1 - 2009

N2 - Parkinson's Disease (PD) is a neurological disorder that has been a hot topic worldwide. Human neurological disorders can be modeled in animals like rats and monkeys using standardized procedures that recreate specific pathogenic events and their behavioral outcomes. Different methods have been proposed to detect and verify the efficiency and effectiveness of such models. However, the inner scheme to detect and predict PD at the early stage is still a difficult problem. In this paper, a Conditional Random Fields (CRFs) based approach for PD image detection and prediction is presented. Machine learning techniques are discussed that proved to be useful in detecting and predicting PD in animal models.

AB - Parkinson's Disease (PD) is a neurological disorder that has been a hot topic worldwide. Human neurological disorders can be modeled in animals like rats and monkeys using standardized procedures that recreate specific pathogenic events and their behavioral outcomes. Different methods have been proposed to detect and verify the efficiency and effectiveness of such models. However, the inner scheme to detect and predict PD at the early stage is still a difficult problem. In this paper, a Conditional Random Fields (CRFs) based approach for PD image detection and prediction is presented. Machine learning techniques are discussed that proved to be useful in detecting and predicting PD in animal models.

UR - https://www.scopus.com/pages/publications/84864915909

M3 - 会议稿件

AN - SCOPUS:84864915909

SN - 9781601321190

T3 - Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009

SP - 147

EP - 152

BT - Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009

T2 - 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009

Y2 - 13 July 2009 through 16 July 2009

ER -

Yuan P, Zhang B, Li J, Wang J, Gao G. A machine learning technique to detect and predict parkinson's disease. In Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009. 2009. p. 147-152. (Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009).

A machine learning technique to detect and predict parkinson's disease

Abstract

Publication series

Conference

UN SDGs

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