Can C-Reactive Protein (CRP) Time Series Forecasting be Achieved via Deep Learning?

IEEE Access

Brendon J. Coventry¹  Anahita Fouladzadeh²  Derek Abbott³  Mohsen Dorraki³  Stephen J. Salamon³  Andrew Allison³ 

[1] Centre for Biomedical Electrical Engineering, The University of Adelaide, Adelaide, SA, Australia;Centre for Cancer Biology, University of South Australia and SA Pathology, Adelaide, SA, Australia;School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia;

关键词: Biomedical engineering;    forecasting;    autoregressive modeling;    ARIMA;    machine learning;    deep learning;   

DOI  :  10.1109/ACCESS.2019.2914473

来源: DOAJ

【 摘 要 】

C-reactive protein (CRP) is a biomarker of inflammation and is widely considered as an indicator of cancer prognosis, risk, and recurrence in clinical experiments. Investigating the properties and behaviors of CRP time series has recently emerged as an area of significant interest in informing clinical decision making. The area of cancer immunotherapy is a key application where CRP forecasting is critically needed. Therefore, predicting the future values of a CRP time series can provide useful information for clinical purposes. In this paper, we focus on CRP time series forecasting, comparing autoregressive integrated moving average (ARIMA) modeling with deep learning. The CRP data are obtained from 24 patients with melanoma. This paper using CRP data indicates that deep learning provides significantly reduced prediction error compared to ARIMA modeling.

【 授权许可】

Unknown