A feasible machine learning framework for diagnosing stroke patients versus mimic conditions incorporating extracellular vesicle characterization and EHR features fusion

Abstract Stroke remains a leading cause of morbidity and mortality worldwide, necessitating rapid and accurate diagnosis to improve patient outcomes. The gold standard in stroke diagnosis relies on a costly and time-consuming combination of clinical evaluation and neuroimaging techniques, while also being associated with misdiagnoses due to mimicking conditions. This highlights the need for improving the current healthcare system towards faster, more accurate, and low-resource diagnostics. In this direction, this study presents the development of a novel artificial intelligence-driven decision support system for stroke diagnosis using machine learning techniques to fuse standard EHR variables (electronic health records) with information derived from extracellular vesicles (EVs) in blood plasma, which hold promise as diagnostic markers due to their association with stroke pathophysiology. As such, flow cytometry was employed to measure blood samples from 140 patients suffering from stroke or stroke-mimicking conditions. The derived EV-features, including statistical characteristics of their diameters’ distribution and concentration, were integrated with demographical, clinical, and biochemical patient data to form a comprehensive feature set. By utilizing a decision tree learning model, the proposed system achieved a classification weighted accuracy of 86%. This achievement indicates the approach’s potential for integrating cutting-edge photonics-based signals with standard HER information to improve clinical decision-making in emergency settings and for aiding treatment strategies in stroke care.