Temporal Pattern Mining for Analysis of Longitudinal Clinical Data:
Identifying Risk Factors for Alzheimer's Disease
- URL: http://arxiv.org/abs/2209.04793v1
- Date: Sun, 11 Sep 2022 05:44:06 GMT
- Title: Temporal Pattern Mining for Analysis of Longitudinal Clinical Data:
Identifying Risk Factors for Alzheimer's Disease
- Authors: Annette Spooner, Gelareh Mohammadi, Perminder S. Sachdev, Henry
Brodaty, Arcot Sowmya
- Abstract summary: The method is applied to a real-world study of Alzheimer's disease (AD), a progressive neurodegenerative disease that has no cure.
The patterns discovered were predictive of AD in survival analysis models with a Concordance index of up to 0.8.
- Score: 9.764638397706719
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A novel framework is proposed for handling the complex task of modelling and
analysis of longitudinal, multivariate, heterogeneous clinical data. This
method uses temporal abstraction to convert the data into a more appropriate
form for modelling, temporal pattern mining, to discover patterns in the
complex, longitudinal data and machine learning models of survival analysis to
select the discovered patterns. The method is applied to a real-world study of
Alzheimer's disease (AD), a progressive neurodegenerative disease that has no
cure. The patterns discovered were predictive of AD in survival analysis models
with a Concordance index of up to 0.8. This is the first work that performs
survival analysis of AD data using temporal data collections for AD. A
visualisation module also provides a clear picture of the discovered patterns
for ease of interpretability.
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