Projects Archive

2020 COVID Response: Jump Simulation Projects

Remote state anxiety detection and monitoring using multimodal wearable sensors

Investigators: Manuel E. Hernandez, PhD, UIUC; Elizabeth Hsiao-Wecksler, PhD, UIUC; Richard Sowers, PhD, UIUC; Brent Roberts, PhD, UIUC; Susan Caldecott-Johnson, MD, UICOMP, OSF HealthCare Children’s Hospital of Illinois; Jean Clore, PhD, UICOMP
 
In frontline health care workers, recent evidence suggests increased depression, anxiety, insomnia and distress due to the COVID-19 pandemic. Even without COVID-19, physician trainees face mental health challenges as they provide care and learn clinical best practices. This project will integrate data from a suite of wearable sensors to quantify symptoms of stress and anxiety in physician trainees. The idea is to use information gleaned from sensors to monitor and potentially improve wellbeing before mental health disorders develop.

Spatio-temporal Analysis with Tensor Factorization and Visualization for Pediatric Mobile Vaccination

Investigators: Jimeng Sun, PhD, UIUC; Mary Stapel, MD, OSF HealthCare; Scott Barrows, OSF HealthCare; Adam Cross, MD, OSF HealthCare; Elise Albers, OSF HealthCare; Ginger Barton, OSF HealthCare; Michelle Sheppard, OSF HealthCare; George Heintz, MSPH, MSE, UIUC; Yaroslav Daniel Bodnar, MD, OSF HealthCare, UICOMP

This project proposes to use AI technology to understand and improve the pediatric population health challenge of timely vaccination. With the help of AI, the project will visualize spatio-temporal patterns, identify critical geographic areas with the most concerning rates of under-vaccination, predict the supply need and deploy mobile immunization units to increase vaccination rates for those areas. This will improve vaccination rates in high-risk zip codes, revealing barriers around access to care and other social determinant obstacles.

 

Toward Automated Diagnosis of Seizures and 3D Representation of SEEG Clinical Data

Investigators: Matthew Bramlet, MD, UICOMP, OSF HealthCare; Brad Sutton, PhD, UIUC; Yogatheesan Varatharajah, PhD, UIUC; Andres Maldonado, MD, UICOMP, OSF HealthCare; Michael Xu, MD, PhD, UICOMP, OSF HealthCare
 
Some patients with seizures face debilitating effects that pharmacologic therapy cannot treat. These patients are left with surgery as their best option that requires an invasive procedure (stereotactic-electroencephalography or SEEG) to pinpoint the origin of these seizures. This project will present surgeons with a stereoscopic 3D model to give surgeons a better mental representation of where seizures are occurring. The group also wants to develop an automated interpretation algorithm of SEEG tracings, and create predictive algorithms to reduce invasive testing.

 

Video enhanced neurology (VEN)

Investigators: Chris Zallek, MD, OSF HealthCare; George Heintz, MSPH, MSE, University of Illinois at Urbana-Champaign (UIUC); and Steven Kastelein, OSF HealthCare
 
The number of individuals in need of neurology evaluations continues to outpace the number of available neurologists. This can create delays in diagnoses and treatment for some patients. This project will develop an infrastructure allowing clinicians to use video recorded neurological exams to reliably communicate findings to neurologists as well as improve monitoring for those already diagnosed with neurodegenerative conditions.

 

MedLang Phase II: An intelligent medical record

Investigators: William Cope, PhD, UIUC; Cheng Xiang Zhai, PhD, UIUC; Mary Kalantzis, PhD, UIUC; Richard Tapping, PhD, University of Illinois College of Medicine Peoria (UICOMP); Yerko Berrocal, MD, UICOMP; Duncan Ferguson, PhD, UIUC; Jessica Hanks, MD, OSF HealthCare, UICOMP; Meenakshy Aiyer MD, UICOMP, OSF HealthCare
 
The goal of this project is to design an intelligent medical record that would work hand-in-hand with the electronic medical record for more precise documentation of cases, and aid in medical decision-making as well as rapid, accurate diagnoses. It would also include machine learning capabilities as more cases are fed into the system.

 

Visualizations of social communication behavior of children with autism

Investigators: Karrie Karahalios, PhD, UIUC; Siraj Siddiqi, MD, OSF HealthCare; David Forsyth, PhD, UIUC; Mark Hasegawa-Johnson, PhD, UIUC; Hedda Meadan, PhD, BCBA-D, UIUC
 
Given that many children with autism spectrum disorder have deficits and delays in communication skills, researchers have been exploring ways to diagnose children early and begin with interventions at a very young age. Much of this begins with improving communication between parents and clinicians. This project’s objective is to develop a series of digital, interactive and visual tools to do just that. The idea is that these visualizations could mitigate challenges in discussions between parents and providers.

 

Early detection of developmental disorders via a remote sensing platform

Investigators: Nancy McElwain, PhD, UIUC; Susan Caldecott-Johnson, MD, UICOMP, OSF HealthCare Children’s Hospital of Illinois; Mark Hasegawa-Johnson, PhD, UIUC; Siraj Siddiqi, MD, OSF HealthCare; Romit Roy Choudhury, PhD, UIUC
 
Child mental, behavioral and developmental disorders often go undiagnosed and untreated, thus increasing risk of spiraling disturbance well beyond childhood. This project will provide “proof of concept” for continuous, unobtrusive, large-scale and automated monitoring of young children’s functioning within the home environment, using wearable sensors. In doing so, a long-term objective is to detect potential developmental disorders or delays before such problems become clinically significant.

 

Community-based tele-rehabilitation health network for robotic stroke therapy

Investigators: T. Kesavadas, UIUC; Dusan Stipanovic, PhD, UIUC; Anne Horowitz, OTR/L, CSRS, MSCS, OSF HealthCare
 
Existing robot-based rehabilitation systems lack effective methods to monitor and enforce a patients’ participation in therapy. We propose to develop a community-based, networked robotic therapy system. This system uses a home-based haptic interface for rehabilitation of fine motor skills, with assistance from a remote external agent, such as a therapist, caregiver or artificial intelligence, who monitors progress and accordingly modifies the therapy regimen.
 

MedLang Phase Il: A Concept Mapping Tool for Case Analysis by Medical Students and Researchers

Investigators: William Cope, PhD, UIUC; Cheng Xiang Zhai, PhD, UIUC; Mary Kalantzis, PhD, UIUC; Richard Tapping, PhD, UICOMP; Yerko Berrocal, MD, UICOMP; Jessica Hanks, MD, OSF HealthCare, UICOMP; Meenakshy Aiyer MD, UICOMP, OSF HealthCare
 
This project extends the recently completed Jump ARCHES project in which the group prototyped MedLang, an ontology-based medical concept mapping tool. The goal is to apply the tool to the analysis of single medical cases by medical students and researchers, and the addition of an artificial intelligence (AI) component to its suggestion system. The benefits of this tool for medical students will be to provide a rigorous learning space for the development of critical clinical thinking and offer a web-based infrastructure which will allow speedy peer review of cases and their accompanying concept maps.

 

Soft and Dexterous Service Robot Configurations to Support Healthcare at Home for Older Adults

Investigators: Girish Krishnan, PhD, UIUC; Wendy Rogers, PhD, UIUC; Ryan Riech, MD, MPH, OSF HealthCare
 
An increasing number of older adults live independently but have health care conditions that must be managed – both chronic and acute. The goal of this project is to investigate the effectiveness of soft robotic configurations in offering effective telehealth solutions, and understanding the social and behavioral aspects of how a robot builds trust with its user.

 

Optimal deployment of cancer prevention through digital health workers

Investigators: Sarah de Ramirez, MD, OSF HealthCare, UICOMP; Hyojung Kang, PhD, UIUC; Lavanya Marla, PhD, UIUC; Roopa Foulger, OSF HealthCare; Mackenzie McGee, MD, OSF HealthCare; Abby Lotz, OSF HealthCare; Melinda Cooling, APRN, OSF HealthCare
 
This project proposes to develop a Digital Health Worker (DHW) program to use multiple varying digital modalities to decrease the socio-economic and racial disparities in cancer screening and mortality, specifically with breast cancer. Through the use of data science, this project will explore the most effective ways to deploy digital interventions for the promotion of cancer screening among populations with historically low screening rates and high mortality rates. Data analysis will also help optimize the DHW program to maximize screening rates, and provide an understanding of how to apply these practices to other types of cancers—especially among underserved populations.

 

A training simulator for Clinical Breast Examination (CBE)

Investigators: Anusha Muralidharan, UIUC; Dr. Sarah de Ramirez, MD, OSF HealthCare, UICOMP; Thenkurussi Kesavadas, PhD, UIUC; Rohit Bhargava, PhD, UIUC; Dr. Sandhya Pruthi, MD, Mayo Clinic; Kimberly Michelle Bolin, National Consortium of Breast Centers
 
This project proposes to develop a high-fidelity training simulator to train health professionals on clinical breast examination techniques. It will also provide clinical evaluation to result in diagnosis of breast cancer at earlier stages, resulting in improved outcomes when followed with timely and appropriate treatment. The project uses current state-of-the-art technology to improve training using real-time performance analysis and mimics a realistic environment, giving medical professionals the flexibility of practicing as many times as they want in order to master the skill.

 

7-Tesla MRI Imaging of Severe Traumatic Brain injury

Investigators: Paul M. Arnold, MD, FACS, Carle Foundation Hospital; Andrew Webb, PhD, Carle Foundation Hospital; Ravishankar Iyer, PhD, UIUC; Brad Sutton, PhD, UIUC; George Heintz, MSPH, MSE, UIUC; Dzung Dinh, MD, UICOMP, OSF HealthCare
 
The goal of this study is to image patients with traumatic brain injuries (TBI) by using high-field MRI, specifically the 7-Tesla. This type of imaging is expected to provide rich, previously unavailable information about lesions to diagnose what the effect of TBI could be. Better understanding of lesions can provide more detailed information about the extent of an injury and the cognitive processes that might be affected six months after injury. This will aid in the development of analytic tools to guide clinicians in decision-making and prognosis.

A Rapid and Affordable Virus Test for Early Warning of a Pandemic

This initiative will develop a test kit that extracts nucleic acid sequences from a sample of blood or saliva which are amplified with primers and new technology that can look for biomarkers of the virus using colorimetry so detection by naked eyes is possible. Researchers hope to develop an all-in-one test kit costing no more than $5 USD.


Converting a Microwave Oven into a Mask Sterilization Unit

This proposal will investigate the effectiveness for sterilization and disinfection to offer additional research to initial reports that indicate microwaves can be an effective microbicide.


Data Driven Analytics to Predict the Dynamics of the COVID 19 Outbreak and the Impact on Healthcare Providers, Resources, and Communities

This project proposes to use a novel analytical approach which incorporates artificial intelligence, data analytics, and machine learning to drive solutions to improve outcomes of the COVID-19 virus.


Rapid, Contactless Vital Signs Collection Using Computer Vision and Consumer Technologies

The goal of the proposal is to develop a computer vision algorithm for rapidly assessing an individual’s key vital signs (temperature, heart rate, respiratory rate, and blood pressure) relevant to COVID-19 utilizing a consumer grade camera.


Secure Federated Learning for Collaborative Diagnostics

This proposal leverages modern cryptographic tools to introduce a computational and software features for securely training predictive models using huge data sets distributed over several medical establishments; while ensuring patient privacy.


Testing the Filtration Efficiency of N95 Respirators for Health-Care Employees and Protecting Public Health in Pandemic Flu Emergencies

This project proposes to solve the problem of quality assurance testing for Do-It-Yourself or rapidly made personal protective equipment (PPE).


The COVID-19 Keeping Safe Program

To help communities open back up in the safest way possible, this platform will expand on the OSF Pandemic Health Worker Program for more extensive monitoring of individuals before, during, and after exposure to COVID-19.


Ventilator Duplication Kit

Aims to develop a rapidly deployable kit for using a single ventilator among multiple patients with varying ventilation needs by tailoring the delivered breaths to each individual.


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