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