Authors:
Zina Zammel
;
Nesrine Khabou
;
Lotfi Souifi
and
Ismael Bouassida Rodriguez
Affiliation:
ReDCAD Laboratory, ENIS, University of Sfax, Tunisia
Keyword(s):
Time Series Prediction, Healthcare, Systematic Literature Review.
Abstract:
Technology has solved many of humanity’s complex problems. Furthermore, healthcare providers and researchers are working together to achieve precision medicine, which is the goal of tailoring medical treatment to the individual characteristics of each patient. As a result, patients will receive better care. In this context, healthcare benefits from Time Series Prediction (TSP) models to improve service levels. TSP models have been successfully used to predict a variety of outcomes, such as patient readmission rates, disease progression, and treatment effectiveness. This study presents a systematic literature review (SLR) focusing on TSP models in healthcare. Based on a systematic search of IEEE, Science Direct, Springer, Hyper Articles en Ligne (HAL), and ACM, 50 articles published between 2018 and 2023 were identified. A review of predictive use cases in healthcare and the TSP models used for them has been conducted in this paper. We classified these models into four categories such
as statistical models, Deep Learning (DL) models, Machine Learning (ML) models and Hybrid models.
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