Predicting Ischemic Stroke Outcome Using Deep Learning Approaches.

Zhennan Huang,Zhongrui Wang,Gang Fang

doi:10.3389/fgene.2021.827522

Abstract

Predicting functional outcomes after an Ischemic Stroke (IS) is highly valuable for patients and desirable for physicians. This facilitates physicians to set reasonable goals for patients and cooperate with patients and relatives effectively, and furthermore to reach common after-stroke care decisions for recovery and make exercise plans to facilitate rehabilitation. The objective of this research is to apply three current Deep Learning (DL) approaches for 6-month IS outcome predictions, using the openly accessible International Stroke Trial (IST) dataset. Furthermore, another objective of this research is to compare these DL approaches with machine learning (ML) for performing in clinical prediction. After comparing various ML methods (Deep Forest, Random Forest, Support Vector Machine, etc.) with current DL frameworks (CNN, LSTM, Resnet), the results show that DL doesn’t outperform ML significantly. DL methods and reporting used for analyzing structured medical data should be developed and improved.

Highlights

Stroke is one of the leading causes of death and permanent disability in the last 20 years globally (Global Burden of Disease Collaborative Network, 2018; World Health Organization, 2018)
The results show that there is no decrease in predicting performance using both machine learning (ML) and Deep Learning (DL) after eliminating the five least important features
Classic ML algorithms and current DL frameworks are adopted to predict the outcomes of Ischemic Stroke (IS) in International Stroke Trial (IST) dataset

Summary

Introduction

Stroke is one of the leading causes of death and permanent disability in the last 20 years globally (Global Burden of Disease Collaborative Network, 2018; World Health Organization, 2018). Predicting a patient’s functional outcomes precisely after a stroke will help physicians in managing an appropriate long-term plan for early rehabilitation. It guides clinicians in setting realistic goals, provides accurate information to patients and their caregivers, and facilitates the creation of an early discharge plan (Veerbeek et al, 2011). Accurate prediction of functional outcomes and reperfusion may potentially improve stroke care, as it can guide selecting the most beneficial treatment option for the individual patient: to perform or to refuse EVT. Several medical communities have created and developed scores and methods that can predict the patient’s functional outcomes after a stroke effectively by only using data readily collected at admission (Ntaios et al, 2012; Hilbert et al, 2019).

Objectives

Methods

Findings

Conclusion