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신산업융합대학
식품영양학과
Journal papers
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Intra-person multi-task learning method for chronic-disease prediction
Title
Intra-person multi-task learning method for chronic-disease prediction
Authors
Kim
;
Gihyeon
;
Lim
;
Heeryung
;
Yunsoo
;
Kwon
;
Oran
;
Choi
;
Jang-Hwan
Ewha Authors
권오란
;
최장환
SCOPUS Author ID
권오란
; 최장환
Issue Date
2023
Journal Title
Scientific Reports
ISSN
2045-2322
Citation
Scientific Reports vol. 13, no. 1
Publisher
Nature Research
Indexed
SCIE; SCOPUS
Document Type
Article
Abstract
In the medical field, various clinical information has been accumulated to help clinicians provide personalized medicine and make better diagnoses. As chronic diseases share similar characteristics, it is possible to predict multiple chronic diseases using the accumulated data of each patient. Thus, we propose an intra-person multi-task learning framework that jointly predicts the status of correlated chronic diseases and improves the model performance. Because chronic diseases occur over a long period and are affected by various factors, we considered features related to each chronic disease and the temporal relationship of the time-series data for accurate prediction. The study was carried out in three stages: (1) data preprocessing and feature selection using bidirectional recurrent imputation for time series (BRITS) and the least absolute shrinkage and selection operator (LASSO); (2) a convolutional neural network and long short-term memory (CNN-LSTM) for single-task models; and (3) a novel intra-person multi-task learning CNN-LSTM framework developed to predict multiple chronic diseases simultaneously. Our multi-task learning method between correlated chronic diseases produced a more stable and accurate system than single-task models and other baseline recurrent networks. Furthermore, the proposed model was tested using different time steps to illustrate its flexibility and generalization across multiple time steps. © 2023, The Author(s).
DOI
10.1038/s41598-023-28383-9
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