Personalized Spiking Neural Network Models of Clinical and Environmental Factors to Predict Stroke

Maryam Doborjeh, Zohreh Doborjeh, Alexander Merkin, Rita Krishnamurthi, Reza Enayatollahi, Valery Feigin, Nikola Kasabov

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
24 Downloads (Pure)

Abstract

The high incidence of stroke occurrence necessitates the understanding of its causes and possible ways for early prediction and prevention. In this respect, statistical methods offer the “big picture,” but they have a weak predictive ability at an individual level. This research proposes a new personalized modeling method based on computational spiking neural networks (SNN) for the identification of causal associations between clinical and environmental time series data that can be used to predict individual stroke events. The method is tested on 804 stroke patients. Given a clinical data set of patients who experienced a stroke in the past and the corresponding environmental time-series data for a selected time-window before the stroke event, the method identifies the clusters of individuals with a high risk for stroke under similar conditions. The methodology involves a pipeline of processes when creating a personalized model for an individual x: (1) selecting a group of individuals Gx with similar personal records to x; (2) training a personalized SNN x model of several days of environmental data related to the Gx group to predict the risk of stroke for x at least one day earlier; (3) model interpretability through 3D visualization; (4) discovery of personalized predictive markers. The results are twofold, first proposing a new computational methodology and second presenting new findings. It is found that certain environmental factors, such as SO 2, PM 10, CO, and PM 2.5, increase the risk of stroke if an individual x belongs to a certain cluster of people, characterized by a combination of family history of stroke and diabetes, overweight, vascular/heart disease, age, and other. For the used population data, the proposed method can predict accurately individual risk of stroke before the day of the stroke. The paper presents a new methodology for personalized machine learning methods to define subgroups of the population with a high risk of stroke and to predict early individual risk of the stroke event. This makes the proposed cognitive computation method useful to reduce morbidity and mortality in society. The method is broadly applicable for predicting individual risk of other diseases and mental health conditions.

Original languageEnglish
Pages (from-to)2187–2202
Number of pages16
JournalCognitive Computation
Volume14
Issue number6
Early online date10 Aug 2022
DOIs
Publication statusPublished (in print/issue) - 30 Nov 2022

Bibliographical note

Funding Information:
Open Access funding enabled and organized by CAUL and its Member Institutions. This research was supported by a research grant from the internal SRIF funding of the National Institute for Stroke and Applied Neurosciences (NISAN) and Knowledge Engineering and Discovery Research Institute (KEDRI) of Auckland University of Technology, New Zealand.

Funding Information:
The environmental data were provided by Auckland Council. The authors would like to thank Emma Witt for her support with the ARCOS IV data extraction.

Publisher Copyright:
© 2022, The Author(s).

Keywords

  • spiking neural networks
  • personalised modelling
  • stroke
  • computational modelling
  • prediction
  • NeuCube
  • Stroke
  • Computational modelling
  • Prediction
  • Spiking neural networks
  • Personalized modeling

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