Behavior Clusters in Ischemic Stroke using NIHSS

Abstract

AbstractBACKGROUNDStroke is one of the leading causes of death and disability. The resulting behavioral deficits can be measured with clinical scales of motor, sensory, and cognitive impairment. The most common of such scales is the National Institutes of Health Stroke Scale, or NIHSS. Computerized tomography (CT) and magnetic resonance imaging (MRI) scans show predominantly subcortical or subcortical-cortical lesions, with pure cortical lesions occurring less frequently. While many experimental studies have correlated specific deficits (e.g. motor or language impairment) with stroke lesion locations, the mapping between symptoms and lesions is not straightforward in clinical practice. The advancement of machine learning and data science in recent years has shown unprecedented opportunities even in the biomedical domain. Nevertheless, their application to medicine is not simple, and the development of data driven methods to learn general mathematical models of diseases from healthcare data is still an unsolved challenge.METHODSIn this paper we measure statistical similarities of stroke patients based on their NIHSS scores, and we aggregate symptoms profiles through two different unsupervised machine learning techniques: spectral clustering and affinity propagation.RESULTSWe identify clusters of patients with largely overlapping, coherent lesions, based on the similarity of behavioral profiles.CONCLUSIONSOverall, we show that an unsupervised learning workflow, open source and transferable to other conditions, can identify coherent mathematical representations of stroke lesions based only on NIHSS data.