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REVIEW article

Front. Physiol.
Sec. Computational Physiology and Medicine
Volume 15 - 2024 | doi: 10.3389/fphys.2024.1386760
This article is part of the Research Topic Artificial Intelligence for Smart Health: Learning, Simulation, and Optimization View all 9 articles

Predictive Modeling of Biomedical Temporal Data in Healthcare Applications: Review and Future Directions

Provisionally accepted
Abhidnya Patharkar Abhidnya Patharkar 1,2*Fulin Cai Fulin Cai 1,2Teresa Wu Teresa Wu 1,2
  • 1 School of Computing and Augmented Intelligence, Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, Arizona, United States
  • 2 Mayo Center for Innovative Imaging, Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, Arizona, United States

The final, formatted version of the article will be published soon.

    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 must also take into account the data-related constraints during the modeling process. Time series models can be divided into statistical, machine learning, and deep learning models. This review concentrates on the main models within these categories, discussing their capability to tackle the mentioned challenges. Furthermore, this paper provides a brief overview of a technique aimed at mitigating the limitations of a specific model to enhance its suitability for clinical prediction. It also explores ensemble forecasting methods designed to merge the strengths of various models while reducing their respective weaknesses, and finally discusses hierarchical models. Apart from the technical details provided in this document, there are certain aspects in predictive modeling research that have arisen as possible obstacles in implementing models using biomedical data. These obstacles are discussed leading to the future prospects of model building with artificial intelligence in healthcare domain.

    Keywords: Biomedical Temporal Data, Biomedical Data Challenges, Forecasting, Clinical predictive modeling, Temporal Data Modeling Problems, Statistical Time-Series Models, Temporal Machine Learning Models, Deep Temporal Models

    Received: 16 Feb 2024; Accepted: 26 Sep 2024.

    Copyright: © 2024 Patharkar, Cai and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Abhidnya Patharkar, School of Computing and Augmented Intelligence, Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, 85281, Arizona, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.