Abstract
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.
This work was supported by an Open Innovation Grant from Xerox Research Centre India.
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Acknowledgment
We thank Abhishek Tripathi who helped us with our experiments while he was at Xerox Research Centre India.
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Rajan, V., Bhattacharya, S., Shetty, R., Sitaram, A., Vivek, G. (2016). Clinical Decision Support for Stroke Using Multi–view Learning Based Models for NIHSS Scores. In: Cao, H., Li, J., Wang, R. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9794. Springer, Cham. https://doi.org/10.1007/978-3-319-42996-0_16
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