Predictive modeling of biomedical temporal data in healthcare applications: review and future directions.

ABSTRACT: Predictive modeling of clinical time series data is challenging due to various factors. One such difficulty is the existence of missing values, which leads to irregular data. Another challenge is capturing correlations across multiple dimensions in order to achieve accurate predictions. Additionally, it is essential to take into account the temporal structure, which includes both short-term and long-term recurrent patterns, to gain a comprehensive understanding of disease progression and to make accurate predictions for personalized healthcare. In critical situations, models that can make multi-step ahead predictions are essential for early detection. This review emphasizes the need for forecasting models that can effectively address the aforementioned challenges. The selection of models m

SUBMITTER: Patharkar A 

PROVIDER: S-EPMC11519529 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature