TY - GEN
T1 - Clinical decision support for stroke using multi–view learning based models for NIHSS scores
AU - Rajan, Vaibhav
AU - Bhattacharya, Sakyajit
AU - Shetty, Ranjan
AU - Sitaram, Amith
AU - Vivek, G.
PY - 2016/1/1
Y1 - 2016/1/1
N2 - Cerebral stroke is a leading cause of physical disability and death in the world. The severity of a stroke is assessed by a neurological examination using a scale known as the NIH stroke scale (NIHSS). As a measure of stroke severity, the NIHSS score is widely adopted and has been found to also be useful in outcome prediction, rehabilitation planning and treatment planning. In many applications, such as in patient triage in under–resourced primary health care centres and in automated clinical decision support tools, it would be valuable to obtain the severity of stroke with minimal human intervention using simple parameters like age, past conditions and blood investigations. In this paper we propose a new model for predicting NIHSS scores which, to our knowledge, is the first statistical model for stroke severity. Our multi–view learning approach can handle data from heterogeneous sources with mixed data distributions (binary, categorical and numerical) and is robust against missing values – strengths that many other modeling techniques lack. In our experiments we achieve better predictive accuracy than other commonly used methods.
AB - Cerebral stroke is a leading cause of physical disability and death in the world. The severity of a stroke is assessed by a neurological examination using a scale known as the NIH stroke scale (NIHSS). As a measure of stroke severity, the NIHSS score is widely adopted and has been found to also be useful in outcome prediction, rehabilitation planning and treatment planning. In many applications, such as in patient triage in under–resourced primary health care centres and in automated clinical decision support tools, it would be valuable to obtain the severity of stroke with minimal human intervention using simple parameters like age, past conditions and blood investigations. In this paper we propose a new model for predicting NIHSS scores which, to our knowledge, is the first statistical model for stroke severity. Our multi–view learning approach can handle data from heterogeneous sources with mixed data distributions (binary, categorical and numerical) and is robust against missing values – strengths that many other modeling techniques lack. In our experiments we achieve better predictive accuracy than other commonly used methods.
UR - http://www.scopus.com/inward/record.url?scp=84978785848&partnerID=8YFLogxK
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U2 - 10.1007/978-3-319-42996-0_16
DO - 10.1007/978-3-319-42996-0_16
M3 - Conference contribution
AN - SCOPUS:84978785848
SN - 9783319429953
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 190
EP - 199
BT - Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2016 Workshops, BDM, MLSDA, PACC, WDMBF, Revised Selected Papers
PB - Springer Verlag
T2 - 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2016 and Workshop on Biologically Inspired Data Mining Techniques, BDM 2016, Workshop on Machine Learning for Sensory Data Analysis, MLSDA 2016, Workshop on Predictive Analytics for Critical Care, PACC 2016 and Workshop on Data Mining in Business and Finance, WDMBF 2016
Y2 - 19 April 2016 through 19 April 2016
ER -
