文献类型：期刊文献

英文题名：Time-Aware Missing Healthcare Data Prediction Based on ARIMA Model

作者：Kong, Lingzhen[1];Li, Guangshun[1];Rafique, Wajid[2];Shen, Shigen[3];He, Qiang[4];Khosravi, Mohammad R.[5,6];Wang, Ruili[7];Qi, Lianyong[1]

机构：[1]Qufu Normal Univ, Sch Comp Sci, Rizhao 276827, Peoples R China;[2]Univ Montreal, Dept Comp Sci & Operat Res, Montreal, PQ H3T 1J4, Canada;[3]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing 312000, Peoples R China;[4]Swinburne Univ Technol, Hawthorn, Vic 3122, Australia;[5]Weifang Univ Sci & Technol, Shandong Prov Univ Lab Protected Hort, Weifang 262799, Shandong, Peoples R China;[6]Persian Gulf Univ, Dept Comp Engn, Bushehr 75169, Iran;[7]Massey Univ, Sch Nat & Computat Sci, Palmerston North 4442, New Zealand

年份：2024

卷号：21

期号：4

起止页码：1042

外文期刊名：IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS

收录：SCI-EXPANDED(收录号：WOS:001290429100030)、、EI(收录号：20223812755215)、Scopus(收录号：2-s2.0-85137932579)、WOS

基金：This work was supported in part by the Natural Science Foundation of Shandong Province under Grant ZR2019MF001, in part by the National Natural Science Foundation of China under Grant 61872219, and in part by the Natural Science Foundation of China under Grant 62177014.

语种：英文

外文关键词：Medical services; Predictive models; Data models; Time factors; Time series analysis; Computational modeling; Redundancy; Missing healthcare data; data prediction; time; ARIMA; truncated SVD

外文摘要：Healthcare uses state-of-the-art technologies (such as wearable devices, blood glucose meters, electrocardiographs), which results in the generation of large amounts of data. Healthcare data is essential in patient management and plays a critical role in transforming healthcare services, medical scheme design, and scientific research. Missing data is a challenging problem in healthcare due to system failure and untimely filing, resulting in inaccurate diagnosis treatment anomalies. Therefore, there is a need to accurately predict and impute missing data as only complete data could provide a scientific and comprehensive basis for patients, doctors, and researchers. However, traditional approaches in this paradigm often neglect the effect of the time factor on forecasting results. This article proposes a time-aware missing healthcare data prediction approach based on the autoregressive integrated moving average (ARIMA) model. We combine a truncated singular value decomposition (SVD) with the ARIMA model to improve the prediction efficiency of the ARIMA model and remove data redundancy and noise. Through the improved ARIMA model, our proposed approach (named MHDPSVD_ARIMA) can capture underlying pattern of healthcare data changes with time and accurately predict missing data. The experiments conducted on the WISDM dataset show that MHDPSVD_ARIMA approach is effective and efficient in predicting missing healthcare data.