Predicting ALS Outcomes Based on Networked Passive Sensors

Abstract

The purpose of the current project is to adapt an existing sensor-based alert system to facilitate early detection of physiological and functional declines among people living with ALS. The current project is a single-site pre-clinical trial to establish the feasibility and preliminary efficacy of the system and to establish a machine learning algorithm for predicting adverse health outcomes based on observed biometric data in people with ALS. Several logistical and regulatory challenges delayed recruiting for the project and subsequent recruiting efforts have been slower than expected. This Annual Technical Report explains steps taken and steps planned to increase recruitment. The Report also outlines progress to date in creating the machine learning algorithm that will be used to analyze data. One manuscript is preparation with intent to submit for publication in 2023. Data collection and analysis are ongoing.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2023
Accession Number
AD1208010

Entities

People

  • Abu Mosa
  • Juliana Earwood
  • Marjorie Skubic
  • Mihail Popescu
  • Vovanti E. Jones
  • William E. Janes
  • Xing Song
  • Zachary Selby

Organizations

  • Curators of the University of Missouri

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Biomedical Research
  • Biometric Security
  • Colitis
  • Computational Science
  • Covid-19
  • Data Analysis
  • Data Mining
  • Data Science
  • Data Sets
  • Detection
  • Health Care
  • Health Services
  • Information Systems
  • Machine Learning
  • Management Personnel
  • Medical Personnel
  • Neuromuscular Diseases
  • Pain
  • Passive Sensors
  • Personnel Management
  • Predictive Modeling
  • Public Health
  • Risk Factors
  • Standards
  • Therapy
  • Universities

Readers

  • Clinical Trial Research.
  • Sensor Fusion and Tracking Systems.
  • Systems Analysis and Design

Technology Areas

  • AI & ML