Machine Learning Models Prognosticate Functional Outcomes Better than Clinical Scores in Spontaneous Intracerebral Haemorrhage.

Journal: Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Published Date: Dec 10, 2021

Abstract

OBJECTIVE: This study aims to develop and compare the use of deep neural networks (DNN) and support vector machines (SVM) to clinical prognostic scores for prognosticating 30-day mortality and 90-day poor functional outcome (PFO) in spontaneous intracerebral haemorrhage (SICH).

Authors

  • Mervyn Jun Rui Lim
    Division of Neurosurgery, University Surgical Centre, National University Hospital, Singapore. Electronic address: mervynlim@u.nus.edu.
  • Raphael Hao Chong Quek
    Department of Electrical and Computer Engineering, National University of Singapore.
  • Kai Jie Ng
    Yong Loo Lin School of Medicine, National University of Singapore.
  • Ne-Hooi Will Loh
    Department of Anaesthesia, National University Hospital, Singapore.
  • Sein Lwin
    Division of Neurosurgery, University Surgical Centre, National University Hospital, Singapore.
  • Kejia Teo
    Division of Neurosurgery, University Surgical Centre, National University Hospital, Singapore.
  • Vincent Diong Weng Nga
    Division of Neurosurgery, University Surgical Centre, National University Hospital, Singapore.
  • Tseng Tsai Yeo
    Division of Neurosurgery, University Surgical Centre, National University Hospital, Singapore.
  • Mehul Motani

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