METHOD FOR ESTABLISHING DEEP LEARNING-BASED MODEL FOR PREDICTING FUNCTIONS AFTER POST-CEREBRAL STROKE EARLY REHABILITATION

• Main Authors: ZHENG, Yu, LI, Jian, PENG, Lijun, LU, Xiao, GU, Zhaohua, GONG, Chen
• Format: Patent
• Language: Chinese; English; French
• Published: 21.12.2023
• Subjects: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS; PHYSICS

A method for establishing a deep learning-based model for predicting functions after post-cerebral stroke early rehabilitation. By constructing a hybrid deep learning model consisting of a convolutional neural network (CNN) and a long short-term memory (LSTM) artificial neural network, and combining clinical data and early rehabilitation related data, the early and accurate prediction of function prognosis after post-cerebral ischemic stroke early rehabilitation is performed; long-term function prediction is performed in combination with a time-dependent modified Rankin scale (mRS), and at the same time, an individualized early rehabilitation strategy is formulated under guidance; function prediction can be performed in the early stage of cerebral stroke by means of the constructed machine learning-based prediction model for function prognosis after post-cerebral ischemic stroke early rehabilitation, i.e., a CNN-LSTM model, so that accurate guidance is provided for the formulation of a subsequent rehabilitati