BIOE faculty research projects funded through Jump ARCHES research and development program


This year, twelve research projects are sharing more than $1,192,000 in funding through the Jump ARCHES research and development program. Five of these exciting projects are co-led by BIOE faculty members.

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This year, twelve research projects are sharing more than $1,192,000 in funding through the Jump ARCHES research and development program.  The funding supports research involving clinicians, engineers, and social scientists to rapidly develop technologies and devices that could revolutionize medical training and health care delivery.

The Spring 2023 grant recipients’ projects focus on key efforts to improve human health, including: developing novel technologies using health data and data analytics to provide tailored health care improvement roadmaps; addressing the evolving standards of care to incorporate personalized precision medicine and genomic best practices; improving care and health literacy of historically underserved populations; assisting in early diagnosis and treatment of neurological disorders; exploring the needs of people with disabilities; and addressing social and behavioral science topics, especially related to issues involving inequity and poverty, social and behavioral health, and the digital revolution, assistive technologies for neurological sciences, autism spectrum disorders, and social influences on the cancer continuum.

Of the 12 projects receiving funds this spring, five are co-led by BIOE faculty members.

Projects Co-Lead by BIOE Faculty:

  • Machine Learning of Standardized DICOM Metadata from Imaging Datasets
Brad Sutton
Brad Sutton

Brad Sutton, PhD, BIOE & CI MED Professor, University of Illinois Urbana-Champaign & Matthew Bramlet, MD, OSF HealthCare

The project aims to develop a machine learning-based algorithm that can categorize image parameters directly from signal intensity variations of 2D medical images to enable efficient pipelines for medical image segmentation. The proposed algorithm is expected to estimate patient and image-acquisition information by utilizing machine learning methods in situations where the DICOM header fields are incomplete or unreliable, ultimately allowing for automated characterization of unknown 3D DICOM imaging datasets.

  • Machine-Guided Staging of Neuroblastic Tumors of Patient Specific 3D Models

Brad Sutton, PhD, BIOE & CI MED Professor, University of Illinois Urbana-Champaign & Daniel Robertson, MD, OSF HealthCare

The OSF HealthCare Children’s Hospital of Illinois is using segmentation services to create 3D models of neuroblastic tumors for pre-surgical planning. The hospital aims to transition from 2D imaging to 3D modeling to increase the reproducibility of staging analysis, establish a new standard for segmented models of neuroblastic tumors and develop machine-guided tools that can improve upon and automate current recommended image-defined risk factors staging.

  • Toward Machine-Learned Aortic Arch Measured Diameters

Brad Sutton, PhD, BIOE & CI MED Professor, University of Illinois Urbana-Champaign & Matthew Bramlet, MD, OSF HealthCare

The original project aims to automate the segmentation and clinical measurement of aortic arch diameters from MRI imaging. The researchers leading this project have successfully completed several steps, including de-identification and curation of datasets, manual segmentation and the development of a novel method for automatically analyzing each aortic arch with promising results, indicating correlation between the automated and clinically derived measurements.

  • Community Health Café: Engaging Digital Innovation and Community-Based Resources to Enhance Health Equities in Underserved Communities
    Joe Bradley
    Joe Bradley

Joe Bradley, PhD, MA, BIOE & CI MED Professor, UIUC & Scott Barrows, MA, OSF HealthCare

The purpose of the community health café is to provide digital access to health and health care resources, including links for assistance with the social determinants of health, health education and connections to public health in underserved communities. The eventual goal is a Medicaid telemedicine option with OSF OnCall. This proposal aims to address critical needs of underserved residents in vulnerable communities and is crucial for their health.

  • AI-Powered Brain Tumor Segmentation
<em>Zhi-Pei Liang</em>
Zhi-Pei Liang

Zhi-Pei Liang, PhD, BIOE & CI MED Professor, UIUC & Matthew Bramlet, MD, OSF HealthCare

This project aims to enhance the detection and monitoring of brain diseases. Phase 1 of the project focuses on accurate delineation and segmentation of brain tumors using a combination of structural and molecular multimodal brain imaging data and deep learning. The proposed work includes developing brain atlases for AI-powered brain image analysis, computational tools for automated tumor detection and segmentation and evaluating potential clinical applications.

Other funded projects:

  • A Field Experiment to Evaluate the Efficacy of Convenient Health Kiosks

Ujjal Mukherjee, PhD, CI MED Health Innovation Professor, UIUC & Ann Willemsen-Dunlap, CRNA, PhD, OSF HealthCare

This proposal outlines a field experiment to evaluate the efficacy of health kiosks supported by community health workers (CHWs) in delivering first line preventive health screenings to rural and underserved communities. The project is intended to lead to large-scale development and deployment of health kiosks with the goal of positively impacting social determinants of health and long-term health status of those served. This project received over $100 thousand in funding.

  • Contextualizing Nursing Needs for the Development of Retention-Support App

Ann-Perry Witmer, PhD, CI MED Clinical Assistant Professor, UIUC & Sheryl Emmerling, PhD, RN, NEA-BC, OSF HealthCare

The goal of this project is to address the high turnover rate of new nurses by providing a digital app that offers personalized nursing support. The Contextual Engineering (CE) paradigm will be used to assess the needs and values of first-year nurses, including those who have left their positions, to inform the development of the app in the first phase of the project, with the goal of stabilizing the nursing staff, improving the quality of service and reducing operating costs.

  • Knowledge Graph Construction with Large Language Models to Predict DKA Occurrence and Severity

Jimeng Sun, PhD, CI MED Health Innovation Professor, UIUC  & Adam Cross, MD, FAAP, OSF HealthCare

Diabetic ketoacidosis (DKA) hospitalizes over 50,000 American children annually, with underprivileged and underserved children at higher risk. This proposal aims to develop a predictive model using patient-specific knowledge graphs generated from clinical data extracted through name entity recognition and language modeling. Clinicians can use the model to identify high-risk diabetic patients and prevent DKA.

  • Optimizing Pharmacologic Management of Behaviors in Patients with Autism

Ravishankar Iyer, PhD, CI MED Professor, UIUC & Adam Cross, MD, FAAP, OSF HealthCare

This proposal aims to provide physicians with a machine-learning model that assists in selecting appropriate medication and dosage strategies for patients with Autism Spectrum Disorder (ASD). By incorporating patient history, genetic information and clinician notes, the model will dynamically adapt the treatment protocol as the patient progresses, ensuring optimal choices for improved behavioral symptoms with a high degree of confidence.

  • Predicting Medication Non-Adherence in Type 2 Diabetes

Hyojung Kang, PhD, University of Illinois Urbana-Champaign & Mary Stapel, MD, OSF HealthCare

Medication adherence is crucial for managing diabetes, but disparities exist, particularly among racial/ethnic minorities and those with lower socioeconomic status. This proposal aims to use data-driven models to identify high-risk individuals and areas for non-adherence to diabetes medication, develop and validate prediction models and implement and evaluate them in clinical practice.

  • Prototype: Intelligent Regulatory Change Management System

ChengXiang Zhai, PhD, University of Illinois Urbana-Champaign & Scott Lowry, MHA, CHC, CCEP, OSF HealthCare

This study proposes an Intelligent Regulatory Change Management (IRCM) System that uses natural language processing and artificial intelligence to track and evaluate public policy actions governing OSF HealthCare. This will enable compliance professionals to identify critical changes and determine appropriate courses of action, reducing manual review and improving quality, safety, privacy risk management and efficiency.

  • STREAM-ED: Simulation to Refine, Enhance and Adapt Management of Emergency

Hyojung Kang, PhD, University of Illinois Urbana-Champaign & William Bond, MD, OSF HealthCare

This study aims to develop practical models combining machine learning, discrete event simulation, and optimization techniques to improve emergency department (ED) resource utilization and address ED overcrowding, which is exacerbated by the COVID-19 pandemic and staffing shortages.

The Jump ARCHES program is a partnership between OSF HealthCare, the University of Illinois Urbana-Champaign (UIUC), and the University of Illinois College of Medicine in Peoria (UICOMP).

Editor's note: The original versions of this article can be found here and here.

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This story was published June 23, 2023.