Creating Interpretable Data-Driven Approaches for Remote Health Monitoring

Authors

  • Alireza Ghods Washington State University

Keywords:

Interpretability, Health Informatics, Time Series Classification

Abstract

We are at a turning point to address the unprecedented challenges we are facing in healthcare systems. With the aging of the population and increasing health disparities in rural areas, healthcare needs assistance from technologies to provide quality care for these populations. In collaboration with clinicians, we seek to meet this need by creating data-driven methods that provide interpretable healthcare models from ubiquitous ambient and wearable sensor data. My doctoral research goal is to introduce novel ways to help clinicians understand patients' health status by developing new visualization tools and interpretable models that analyze human health and behavior from sensor data.

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Published

2021-05-18

How to Cite

Ghods, A. (2021). Creating Interpretable Data-Driven Approaches for Remote Health Monitoring. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15712-15713. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17853

Issue

Section

The Twenty-Sixth AAAI/SIGAI Doctoral Consortium