Uncovering disease regions using pseudo time-series trajectories on clinical trial data

Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010

Volumn 6, Issue , 2010, Pages 2356-2362

  Li, Yuanxi ^a   Tucker, Allan ^a  

^a BRUNEL UNIVERSITY   (United Kingdom)

Author keywords

Classfication; Cross sectional; Time series; Trajectories

Indexed keywords

BREAST CANCER; BUILD TIME; CLASSFICATION; CLINICAL TRIAL; CROSS-SECTIONAL; DISEASE MODELS; DISEASE STATE; DISTANCE METRICS; MEDICAL DATA SETS; PARKINSON'S DISEASE; REAL-WORLD DATASETS; REGIONS OF INTEREST; RESAMPLING METHOD; SYNTHETIC DATA; TRANSITIONAL STATE;

BIOMEDICAL ENGINEERING; GRAPH THEORY; INFORMATION SCIENCE;

TRAJECTORIES;

EID: 78650645128     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BMEI.2010.5639726     Document Type: Conference Paper

References (13)

1. Advanced glaucoma intervention study. 2. Visual field test scoring and reliability. Ophthalmology, 101(8):1445-1455, 1994.
2. J. Bilmes. A gentle tutorial on the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models. Technical Report TR-97-021, ICSI, 1997.
3. D. Garway-Heath, F.Fitzke, and R. Hitchings. Mapping the visual field to the optic disc. Ophthalmology, (107):1809-1815, 2000.
4. D. Kamal, D. Garway-Heath, R. Hitchings, and F.Fitzke. Use of sequential heidelberg retina tomograph images to identify changes at the optic disc in ocular hypertensive patients at risk of developing glaucoma. British Journal of Ophthalmology, Page 993998, 2000.
5. K. Murphy. Dynamic bayesian networks: Representation , inference and learning. PhD Thesis, UC Berkeley, Computer Science Division, 2002.
6. th International Conference on Intelligent Data Analysis (IDA-2007): Lecture Notes in Computer Science 4723, pages 184-194, 2007.
7. N.G. Strouthidis, A. Scott, N.M. Peter, and D.F. Garway-Heath. Optic disc and visual field progression in ocular hypertensive subjects: detection rates, specificity, and agreement. Invest Ophthalmol Vis Sci., 47:2904-2910, 2006.
8. A. Tucker and D. Garway-Heath. The pseudo temporal bootstrap for predicting glaucoma from cross-sectional visual field data. IEEE Transactions on IT in Biomedicine, 2010 (14) 1: 79-85.
9. A. Tucker, V. Vinciotti, D. Garway-Heath, and X. Liu. A spatio-temporal bayesian network classifier for understanding visual field deterioration. Artificial Intelligence in Medicine, (34):163-177, 2005.
10. Quick Cancer Facts, Programmes and projects, Cancer [http://www.who.int/ cancer/en/index.html], World Health Organization, World Cancer Day 2010.
11. World Health Organisation, Parkinson's Disease - A unqiue survey launched, Press Release WHO/71, World Health Organization, 1998. [http://www.who.int/inf-pr-1998/en/pr98-71.html]
12. W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. Computer-derived nuclear features distinguish malignant from benign breast cytology. Human Pathology, 26:792-796, 1995
13. M.A. Little, P.E. McSharry, and S.J. Roberts. Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', [http://archive.ics.uci.edu/ml/datasets/Parkinsons], Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23, 2007