Behavior Clusters in Ischemic Stroke using NIHSS

BACKGROUND Stroke 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. METHODS In 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. RESULTS We identify clusters of patients with largely overlapping, coherent lesions, based on the similarity of behavioral profiles. CONCLUSIONS Overall, 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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by the Department of excellence 2018-2022 initiative of the Italian Ministry of education (MIUR) awarded to the Department of Neuroscience-University of Padua. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: For data of patients of the Saint Louis cohort: the Internal Review Board of Washington University School of Medicine (WUSM) gave ethical approval for this work. For data of patients of the Padua cohort: the Ethics Committee of the Azienda Ospedale Universit&agrave Padova (AOUP) gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data can be made available upon reasonable request to Maurizio Corbettta at maurizio.corbetta{at}unipd.it. * AP : Affinity Propagation. GDM : General Distance Measure. GSM : General Similarity Measure. NIHSS : National Institutes of Health Stroke Scale. RSC : Repeated Spectral Clustering.