Prediction of Functional Indepencence Measure (FIM) gain in acute brain hemorrhage by automated machine learning

Abstract

The aim of this study was to establish a method for estimating functional prognosis in cerebral hemorrhage patients. Supervised automated machine learning using neural network and gradient boosting decision tree algorithm demonstrated that FIM (functional independence measure) gain can be predicted by feature values such as age, sex, location and size of cerebral hematoma and FIM score. For motor FIM gain (total amount of 13-item motor subscale score), features with high contribution rate were listed such as extension site of hematoma, age, size, volume and location of hematoma. These features will be useful to predict patient’s clinical functional recover in rehabilitation conference.