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

• Published in: IEEE access Vol. 7; pp. 59311 - 59320
• Main Authors: Dorraki, Mohsen, Fouladzadeh, Anahita, Salamon, Stephen J., Allison, Andrew, Coventry, Brendon J., Abbott, Derek
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: ARIMA; autoregressive modeling; Autoregressive models; Autoregressive processes; Biomarkers; Biomedical engineering; C-reactive protein; Cancer; CRP; Decision making; Deep learning; Error reduction; Forecasting; Immune system; immunotherapy; machine learning; Mathematical model; Medical prognosis; Modelling; Proteins; recurrent neural networks; Time series; Time series analysis
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2914473

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.