A Study on Clinical Diagnosis of Hemorrhagic Stroke Based on Random Forest and LSTM Neural Networks

Abstract

This study is dedicated to the application of artificial intelligence technology to assist the clinical management of hemorrhagic stroke patients. By combining patients' clinical information and imaging features, we successfully predicted the probability of hematoma expansion, which provides an important reference for clinical decision-making. Further, we modeled the progression of perihematomal edema and investigated the effect of therapeutic interventions on edema progression. Through K-means clustering and least squares, we fitted the model effectively, which provided a basis for the development of personalized treatment plans. In addition, we used LSTM neural network to predict patients' prognostic MRS scores, which provided new ideas for optimizing patients' rehabilitation pathways. These findings will help to improve the treatment outcome of hemorrhagic stroke patients and reduce the burden on society and families, which is of great clinical significance.