Learning to predict health status of geriatric patients from observational data

Yang, Y; Hauptmann, A; Chen, MY; Cai, Y; Bharucha, A; Wactlar, H

Learning to predict health status of geriatric patients from observational data

Yang, Y

Hauptmann, A Chen, MY Cai, Y Bharucha, A Wactlar, H

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Publication Type:: Conference Proceeding
Citation:: 2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012, 2012, pp. 127 - 134
Issue Date:: 2012-07-25

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Field	Value	Language
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Hauptmann, A	en_US
dc.contributor.author	Chen, MY	en_US
dc.contributor.author	Cai, Y	en_US
dc.contributor.author	Bharucha, A	en_US
dc.contributor.author	Wactlar, H	en_US
dc.date.issued	2012-07-25	en_US
dc.identifier.citation	2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012, 2012, pp. 127 - 134	en_US
dc.identifier.isbn	9781467311892	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120916
dc.description.abstract	Data for diagnosis and clinical studies are now typically gathered by hand. While more detailed, exhaustive behavioral assessments scales have been developed, they have the drawback of being too time consuming and manual assessment can be subjective. Besides, clinical knowledge is required for accurate manual assessment, for which extensive training is needed. Therefore our great research challenge is to leverage machine learning techniques to better understand patients health status automatically based on continuous computer observations. In this paper, we study the problem of health status prediction for geriatric patients using observational data. In the first part of this paper, we propose a distance metric learning algorithm to learn a Mahalanobis distance which is more precise for similarity measures. In the second part, we propose a robust classifier based on ℓ 2,1-norm regression to predict the geriatric patients' health status. We test the algorithm on a dataset collected from a nursing home. Experiment shows that our algorithm achieves encouraging performance. © 2012 IEEE.	en_US
dc.relation.ispartof	2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012	en_US
dc.relation.isbasedon	10.1109/CIBCB.2012.6217221	en_US
dc.title	Learning to predict health status of geriatric patients from observational data	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Data for diagnosis and clinical studies are now typically gathered by hand. While more detailed, exhaustive behavioral assessments scales have been developed, they have the drawback of being too time consuming and manual assessment can be subjective. Besides, clinical knowledge is required for accurate manual assessment, for which extensive training is needed. Therefore our great research challenge is to leverage machine learning techniques to better understand patients health status automatically based on continuous computer observations. In this paper, we study the problem of health status prediction for geriatric patients using observational data. In the first part of this paper, we propose a distance metric learning algorithm to learn a Mahalanobis distance which is more precise for similarity measures. In the second part, we propose a robust classifier based on ℓ 2,1-norm regression to predict the geriatric patients' health status. We test the algorithm on a dataset collected from a nursing home. Experiment shows that our algorithm achieves encouraging performance. © 2012 IEEE.

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http://hdl.handle.net/10453/120916