NDLI: Prediction of longterm outcome of neuropsychological tests of MTBI patients using imaging features

Content Provider	IEEE Xplore Digital Library
Author	Minaee, S. Yao Wang Lui, Y.W.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Polytech. Inst. of NYU, New York, NY, USA (Minaee, S.; Yao Wang) \|\| Dept. of Radiol., New York Univ., New York, NY, USA (Lui, Y.W.)
Abstract	Mild Traumatic Brain Injury (MTBI) is a growing public health problem with an underestimated incidence of over one million people annually in the U.S. Neuropsychological tests are used to both assess the patient condition and to monitor patient progress. This work aims to use features extracted from MR images taken shortly after injury to predict the performance of MTBI patients on neuropsychological tests one year after injury. Successful prediction can enable early patient stratification and proper treatment planning. The goal of this project is to determine the most effective features and corresponding prediction method for such prediction. The main challenge is that we have only limited training data, from which we need to develop the prediction method that can be expected to provide accurate prediction results for unseen data. In the machine learning literature, feature selection is usually done to minimize the cross validation error, which still has a chance for overfitting if the available data set is small and noisy. We propose a novel criterion for feature selection, which considers both the cross validation error and the prediction model variance, to further reduce the chance for overfitting. The algorithm is applied to a data set of 15 MTBI patients. The proposed method was able to determine a subset of MR image features for predicting each neuropsychological test to yield both small prediction error and prediction variance.
Starting Page	1
Ending Page	6
File Size	290738
Page Count	6
File Format	PDF
ISBN	9781479930074
DOI	10.1109/SPMB.2013.6736783
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Linear regression Fingers Predictive models Prediction algorithms Data models Injuries
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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