Clinical decision support for stroke using multi–view learning based models for NIHSS scores

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.