Machine Learning–Based Model for Prediction of Outcomes in Acute Stroke

Abstract

Background and Purpose— The prediction of long-term outcomes in ischemic stroke patients may be useful in treatment decisions. Machine learning techniques are being increasingly adapted for use in the medical field because of their high accuracy. This study investigated the applicability of machine learning techniques to predict long-term outcomes in ischemic stroke patients. Methods— This was a retrospective study using a prospective cohort that enrolled patients with acute ischemic stroke. Favorable outcome was defined as modified Rankin Scale score 0, 1, or 2 at 3 months. We developed 3 machine learning models (deep neural network, random forest, and logistic regression) and compared their predictability. To evaluate the accuracy of the machine learning models, we also compared them to the Acute Stroke Registry and Analysis of Lausanne (ASTRAL) score. Results— A total of 2604 patients were included in this study, and 2043 (78%) of them had favorable outcomes. The area under the curve for the deep neural network model was significantly higher than that of the ASTRAL score (0.888 versus 0.839; P <0.001), while the areas under the curves of the random forest (0.857; P =0.136) and logistic regression (0.849; P =0.413) models were not significantly higher than that of the ASTRAL score. Using only the 6 variables that are used for the ASTRAL score, the performance of the machine learning models did not significantly differ from that of the ASTRAL score. Conclusions— Machine learning algorithms, particularly the deep neural network, can improve the prediction of long-term outcomes in ischemic stroke patients.

Functional Outcome Prediction in Ischemic Stroke: A Comparison of Machine Learning Algorithms and Regression Models

“…Our analyses revealed that ML algorithms and logistic regression models had comparable predictive accuracy when validated internally and externally. Our findings buttress current evidence from other published studies (28)(29)(30)(31)(32)(33) that already showed that the logistic regression and ML algorithms had comparable predictive accuracy in empirical clinical studies. A recently published systematic review found no evidence of the superior predictive performance of ML models over logistic regression models in clinical studies (32).…”

Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches

“…Consistent with the findings of previous studies, our study showed that machine learning methods are feasible and applicable for predicting recovery of stroke patients [6][7][8][9]. Furthermore, we expand findings of previous studies by showing that machine learning methods could also make accurate prediction for postintervention improvements of common task-oriented interventions in individuals with chronic stroke.…”

“…Another two studies found model discriminating ability between 77 and 89% using various types of machine learning methods (e.g. support vector machine and logistic regression) in acute stroke patients [6,8]. Similarly, in the present study, we identified prediction accuracy of 85% and 81% and discriminating ability of 89% and 77% with KNN and ANN models.…”

“…For example, Lin et al evaluated whether machine learning models could predict recovery of activities of daily living in acute stroke patients [6]. Other studies assessed whether machine learning models could predict motor and/or cognitive improvement in acute/subacute stroke patients [7][8][9]. Results of these studies were promising with moderate to high accuracy; however, these studies primarily involved inpatient rehabilitation in acute/subacute stroke.…”

Establishment and Comparison of Algorithms for Detection of Primary Angle Closure Suspect Based on Static and Dynamic Anterior Segment Parameters

“…31 An artificial neuron is designed based on the biological neuron itself and receives multiple inputs multiplied by weights; it outputs the sum of the inputs. 31 It is equivalent to logistic regression but can solve more difficult problems with more complex network architectures. 28,30 The price of using complex architectures is that it produces models that are more difficult to interpret.…”