Forecasting rheumatoid arthritis patient arrivals by including meteorological factors and air pollutants.

Autor: Ye Z; Department of Endocrinology, Hangzhou Linping Traditional Chinese Medicine Hospital, No. 101 Yuncheng Street, Linping District, Hangzhou City, Zhejiang Province, China., Ye B; School of Clinical Medicine, Shanxi Datong University, No. 1 Xingyun Street, Datong City, Shanxi Province, China., Ming Z; The Fifth Clinical College, Anhui Medical University, No. 81 Meishan Road, Shushan District, Hefei City, Anhui Province, China., Shu J; Department of Endocrinology, Hangzhou Linping Traditional Chinese Medicine Hospital, No. 101 Yuncheng Street, Linping District, Hangzhou City, Zhejiang Province, China., Xia C; Department of Endocrinology, Hangzhou Linping Traditional Chinese Medicine Hospital, No. 101 Yuncheng Street, Linping District, Hangzhou City, Zhejiang Province, China., Xu L; Medical Department, Hangzhou Linping Traditional Chinese Medicine Hospital, No. 101 Yuncheng Street, Linping District, Hangzhou City, Zhejiang Province, China., Wan Y; Department of Endocrinology, Hangzhou Linping Traditional Chinese Medicine Hospital, No. 101 Yuncheng Street, Linping District, Hangzhou City, Zhejiang Province, China., Wei Z; Department of Algorithms and Technology, Huawei Technologies Co., Ltd., No. 2222 Xinjinqiao Road, Pudong New Area, Shanghai City, China. zzwei94@126.com.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Aug 01; Vol. 14 (1), pp. 17840. Date of Electronic Publication: 2024 Aug 01.
DOI: 10.1038/s41598-024-67694-3
Abstrakt: The burden of rheumatoid arthritis (RA) has gradually elevated, increasing the need for medical resource redistribution. Forecasting RA patient arrivals can be helpful in managing medical resources. However, no relevant studies have been conducted yet. This study aims to construct a long short-term memory (LSTM) model, a deep learning model recently developed for novel data processing, to forecast RA patient arrivals considering meteorological factors and air pollutants and compares this model with traditional methods. Data on RA patients, meteorological factors and air pollutants from 2015 to 2022 were collected and normalized to construct moving average (MA)- and autoregressive (AR)-based and LSTM models. After data normalization, the root mean square error (RMSE) was adopted to evaluate models' forecast ability. A total of 2422 individuals were enrolled. Not using the environmental data, the RMSEs of the MA- and AR-based models' test sets are 0.131, 0.132, and 0.117 when the training set: test set ratio is 2:1, 3:1, and 7:1, while they are 0.110, 0.130, and 0.112 for the univariate LSTM models. Considering meteorological factors and air pollutants, the RMSEs of the MA- and AR-based model test sets were 0.142, 0.303, and 0.164 when the training set: test set ratio is 2:1, 3:1, and 7:1, while they were 0.108, 0.119, and 0.109 for the multivariable LSTM models. Our study demonstrated that LSTM models can forecast RA patient arrivals more accurately than MA- and AR-based models for datasets of all three sizes. Considering the meteorological factors and air pollutants can further improve the forecasting ability of the LSTM models. This novel method provides valuable information for medical management, the optimization of medical resource redistribution, and the alleviation of resource shortages.
(© 2024. The Author(s).)
Databáze: MEDLINE
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