Title: DART Seed Grant Recipient Presentations, Session 2
Presenter(s): Xiao Huang and Ahmed Abu Halimeh
Date: October 26, 2022

Description

This month’s webinar will feature three presentations from the recently awarded DART YR 3 Seed Grant Recipients.

  • Yasir Rahmatallah (UAMS) will present on Machine Learning Approaches for Remote Pathological Speech Assessment for Parkinson’s Disease. This project is part of the LP research theme.
  • Sudeep Bhattacharyya (A-State) and Melody Greer (UAMS) will present on Smart curation and deep learning-based enhancement of social risk data. This project is part of the DC and LP research themes.
  • Jason Causey (A-State) will present on MoDaCoM-TL: Model and Data Compatibility Metric for Transfer Learning. This project is part of the LP research theme.

Machine Learning Approaches for Remote Pathological Speech Assessment for Parkinson’s Disease

The objective of this project is to develop analysis pipelines to detect discriminative speech acoustic features between people with Parkinson’s Disease and healthy controls using voice recordings acquired via phone line. Parkinson’s disease is a neurodegenerative disease of aging that leads to a spectrum of symptoms including speech impairments. Speech impairments usually manifest in the form of decreasing speech intensity and speed, monotone voice, imprecise articulation, change of pitch, tremors, hoarseness, breathiness, and freezing of speech. Diagnostic criteria are based on clinical motor examination, and motor symptoms manifest over years and possibly decades after the pathology starts. Diagnosis may be delayed for millions of patients living in rural under-served areas with limited access to neurological care. The project will harness the predictive power of machine learning methods while increasing the interpretability of the predictions by highlighting the specific impairment speech patterns associated with the most discriminative features. The proposed project will provide a much needed foundation for validation and methodology development studies and for distinguishing speech patterns characteristic of specific phenotypes of Parkinson’s patients.

Smart curation and deep learning-based enhancement of social risk data

TBA

MoDaCoM-TL: Model and Data Compatibility Metric for Transfer Learning

TBA

Presenter Bios

Dr. Yasir Rahmatallah is an assistant professor in the Department of Biomedical Informatics at the University of Arkansas for Medical Sciences. He received his PhD degree in Applied Sciences from the University of Arkansas Little Rock in 2010. His research interests include Bioinformatics and signal processing, with focus on the development of statistical methodology for gene set analysis of large-scale omics datasets. He is the developer and maintainer of the Bioconductor package GSAR which provides statistical methods to detect gene sets with specific alternative hypotheses. The package has been downloaded over 18,000 times between 2015 and 2021 with an average of 2,500 downloads per year. His recent research projects include the secondary use of electronic health records to predict disease development trajectories and treatment factors influencing them, and using machine learning approaches for remote pathological speech assessment for Parkinson’s disease patients. Dr. Rahmatallah supports research in primarily undergraduate institutions in Arkansas as part of the Arkansas INBRE research support core.