Abstract
Background
The recovery process for patients post-cerebral hemorrhage is influenced by various factors. Crucially, multimodal information, including results from rehabilitation function assessments, imaging examinations, and laboratory tests, is essential for evaluating motor function and predicting the rehabilitation outcome in these patients.
Methods
A retrospective analysis was conducted, utilizing data from 315 and 424 patients with post-cerebral hemorrhage. The training set comprised rehabilitation function assessment results, imaging examination data, and laboratory test results, used to establish models for evaluating motor function and predicting rehabilitation outcomes. Clinical characteristics of patients underwent single-factor and multi-factor logistic regression analyses, exploring influencing factors during the recovery process after cerebral hemorrhage. Head CT scans of patients underwent pre-processing, extracting radiomic features for predicting motor function when combined with functional assessment results. Moreover, a GBDT gradient boosting tree model was constructed based on patients' multimodal clinical information and compared with other machine learning models to evaluate rehabilitation outcomes. Separate models for predicting motor function and evaluating rehabilitation were established for patients with cerebral hemorrhage using selected radiomic and clinical features, and the performance of each model was individually assessed.
Results
Following extensive training and validation with a substantial patient records dataset, the receiver operating characteristic (ROC) analysis reveals the excellent performance of the motor function prediction model we developed for patients post-cerebral hemorrhage. In both five-fold and ten-fold cross-validation, the gradient boosting decision tree (GBDT) algorithm demonstrates superior performance in the post-cerebral hemorrhage rehabilitation assessment compared to other machine learning algorithms. Multifactor analysis indicates that, in predicting motor function after cerebral hemorrhage, factors such as albumin, neutrophil count and percentage, triglycerides, coagulation time, and urea are significant. In rehabilitation assessment, meaningful factors include the age of onset, admission modified Barthel Index (mBI) score, and the initiation of rehabilitation.
Conclusion
Our study integrates multimodal information and advanced machine learning algorithms, providing a solution for assessing function and predicting rehabilitation in patients after cerebral hemorrhage. The established models hold the potential to provide decision support for clinicians in clinical practice, promoting the realization of personalized rehabilitation treatment.