Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data.

Journal: Bioengineering (Basel, Switzerland)
Published Date: May 14, 2025

Abstract

Depression and anxiety are common comorbidities of stroke. Research has shown that about 30% of stroke survivors develop depression and about 20% develop anxiety. Stroke survivors with such adverse mental outcomes are often attributed to poorer health outcomes, such as higher mortality rates. The objective of this study is to use deep learning (DL) methods to predict the risk of a stroke survivor experiencing post-stroke depression and/or post-stroke anxiety, which is collectively known as post-stroke adverse mental outcomes (PSAMO). This study studied 179 patients with stroke, who were further classified into PSAMO versus no PSAMO group based on the results of validated depression and anxiety questionnaires, which are the industry's gold standard. This study collected demographic and sociological data, quality of life scores, stroke-related information, medical and medication history, and comorbidities. In addition, sequential data such as daily lab results taken seven consecutive days after admission are also collected. The combination of using DL algorithms, such as multi-layer perceptron (MLP) and long short-term memory (LSTM), which can process complex patterns in the data, and the inclusion of new data types, such as sequential data, helped to improve model performance. Accurate prediction of PSAMO helps clinicians make early intervention care plans and potentially reduce the incidence of PSAMO.

Authors

  • Chien Wei Oei
    Management Information Department, Office of Clinical Epidemiology, Analytics and kNowledge (OCEAN), Tan Tock Seng Hospital, Singapore 308433, Singapore.
  • Eddie Yin Kwee Ng
    School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore. Electronic address: mykng@ntu.edu.sg.
  • Matthew Hok Shan Ng
    Rehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore 308232, Singapore.
  • Yam Meng Chan
    Department of General Surgery, Tan Tock Seng Hospital, Singapore, Singapore.
  • Vinithasree Subbhuraam
    The Digital Health Hub, Austin, TX 78944, USA.
  • Lai Gwen Chan
    Department of Psychiatry, Tan Tock Seng Hospital, Singapore 308433, Singapore.
  • U Rajendra Acharya
    School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Darling Heights, Australia.

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