2022 ARCHES Projects
Fall 2022 Project Awards
Remote state anxiety detection and monitoring using multimodal wearable sensors – Phase 2
- Manuel Hernandez, PhD, University of Illinois Urbana-Champaign
- Elizabeth Hsiao-Wecksler, PhD, University of Illinois Urbana-Champaign
- Jean Clore, PhD, University of Illinois Urbana-Champaign
- Richard Sowers, PhD, University of Illinois Urbana-Champaign
We seek to create and validate a novel wearable sensor-based machine learning and artificial intelligence framework for identifying and predicting mental health symptoms in young adults and examine the association between state anxiety with increased stress due to potential stressors in a medical environment.
Patient safety learning laboratory: Human-centered, utility-focused alarm design to preserve maternal lives and health
- Abigail Wooldridge, PhD, University of Illinois Urbana-Champaign
- William Bond, MD, OSF HealthCare
- Shruti Chakravarthy, PhD, University of Illinois College of Medicine Peoria
- Jonathan Handler, MD, FACEP, FAMIA, OSF HealthCare
- Neelam Verma, PhD, University of Illinois College of Medicine Peoria
The proposed prototype project addresses postpartum hemorrhage, a complex healthcare prediction-alarm-response-intervention challenge. This work will define optimal alarm methods, utility, timing, and presentation capped by simulation testing of alarm integration.
Exposomic equity, a new paradigm: What is it, why is it important and how can we communicate its effect on the health of all communities (using human-centered design)?
- Noël Adams, MBA, OSF HealthCare
- Scott Barrows, MA, OSF HealthCare
- Ruby Mendenhall, PhD, University of Illinois Urbana-Champaign
- Rachel Switzky, MFA, University of Illinois Urbana-Champaign
- Elise Albers, MBA, MPH, OSF HealthCare
- Joe Bradley, PhD, University of Illinois Urbana-Champaign
- Leslie McKnight, PhD, Peoria City/County Health Department
- Mary Stapel, MD, OSF HealthCare
This project suggests an interactive and innovative way to provide “education to empower.” Also learners, especially youth of color experiencing health disparities, will evaluate their own environmental risks and help assist in the development of new methodologies to change their risks exposures.
Toward automated diagnosis and 3D visualization of seizure onset zones from SEEG, MRI and CT clinical data
- Brad Sutton, PhD, University of Illinois Urbana-Champaign
- Andres Maldonado, MD, OSF HealthCare
- Jennifer Amos, PhD, University of Illinois Urbana-Champaign
- Matthew Bramlet, MD, OSF HealthCare
- Yogatheesan Varatharajah, PhD, University of Illinois Urbana-Champaign
- Michael Xu, MD, OSF HealthCare
Using multimodal imaging combined with stereo electroencephalography (SEEG) to enable localization of the seizure foci and identification of nearby critical anatomy to plan the patient-specific intervention. This will transform epilepsy surgical decision making by translating existing medical data into 3D and 4D visuals; significantly increasing surgeon confidence, intervention planning, and improving patient outcomes.
High-fidelity pediatric heart for surgical simulation of mitral valve replacement
- Sameh Tawfick, PhD, University of Illinois Urbana-Champaign
- Harma Turbendian, MD, OSF HealthCare
- Matthew Bramlet, MD, OSF HealthCare
The team will develop a high-fidelity replication of pediatric heart left ventricle (LV) pulse profile and ventricular end-diastolic volume. They will also complete instrumented platform for the high-fidelity beating pediatric left ventricle which will be connected to a blood circulatory system.
The development of non-wearable leg movement monitoring system with a pressure sensor mat
- Sasikanth Gorantla, MD, OSF HealthCare
- Yuxiong Wang, PhD, University of Illinois Urbana-Champaign
- Kyle Formella, MS, OSF HealthCare
Our project aims to create a convenient novel leg movement monitoring system based on pressure sensing technology, primarily to enhance the assessment of motor symptom burden in restless legs syndrome (RLS) patients.
Microwells-based platform for rapid and simultaneous detection of viruses from milliliters of whole blood
- Rashid Bashir, PhD, University of Illinois Urbana-Champaign
- Enrique Valera, PhD, University of Illinois Urbana-Champaign
- John Farrell, MD, OSF HealthCare
This platform will automate the lab testing to minimize handling and allow for simultaneous detection. This will also minimize the amount of sample required for lab testing.
Correlation of mental health screening and accelerated growth trajectories during the pandemic
- Amy Christison, MD, OSF HealthCare
- Ana Lucic, PhD, University of Illinois Urbana-Champaign
- John Wolff, University of Illinois Urbana-Champaign
There is a bidirectional relationship between mental health concerns and childhood obesity. Therefore the team will develop a propensity risk score calculation using machine learning based on the initial analysis in order to inform clinicians or healthcare systems about at-risk pediatric patients for social-emotional health concerns warranting screening and potential referral.
Algorithmic risk screening for and genetic diagnosis of familial hypercholesterolemia
- Roopa Foulger, OSF HealthCare
- Jimeng Sun, PhD, University of Illinois Urbana-Champaign
- Tinoy Kizhakekuttu, MD, OSF HealthCare
- Brent Cross, MS, OSF HealthCare
- Jonathan Handler, MD, FACEP, FAMIA, OSF HealthCare
- Mauro Montevecchi, MD, OSF HealthCare
The goals of this project are to build an automated process/tool for identifying patients who are eligible for genetic counseling and genetic testing via an algorithmic approach and to evaluate the downstream services completed following patient identification.
Mixed reality (MR) approach to create cyber physical training simulation environments for neonatal procedures
- Avinash Gupta, PhD, University of Illinois Urbana-Champaign
- Nicole Rau, MD, OSF HealthCare
- Muhammad Javed, MD, OSF HealthCare
- Harris Nisar, University of Illinois Urbana-Champaign
The goal is to develop a guided MR neonatal training environment for needle thoracentesis: Based on a consolidated curriculum created the team will develop a prototype of a guided MR simulator for this procedure. The team will also optimize the capabilities of the MR system for real time feedback
Developing an easy-to-use application for combating health misinformation in health care settings
- Kevin Leicht, PhD, University of Illinois Urbana-Champaign
- ChangXiang Zhai, PhD, University of Illinois Urbana-Champaign
- Mary Stapel, MD, OSF HealthCare
- Prasanna Balaprakash, PhD, Argonne National Laboratory
- James Evans, PhD, University of Chicago
- Brant Houston, University of Illinois Urbana-Champaign
- Peter Ondish, PhD, University of Illinois Urbana-Champaign
- Joseph Yun, PhD, University of Pittsburgh
The goal is to develop an application that provides seamless access to current, health-related social media misinformation for healthcare professionals, and assess ways to integrate updates about health-related social media misinformation into health care professionals’ workflow.
Proof of concept: Intelligent compliance response support system
- ChengXiang Zhai, PhD, University of Illinois Urbana-Champaign
- John Evancho, JD, OSF HealthCare
- Gail Sheehan, OSF HealthCare
- Rachel McKinzie, MMBS, OSF HealthCare
- George Heintz, MS, University of Illinois Urbana-Champaign
- Health care GRC systems are challenged by high regulation density in clinical and administrative environments and frequent regulation amendments. We envision that Natural Language
Processing (NLP) techniques can be used to monitor, mine and prioritize reported amendments, make inferences to advance knowledge, and create additional methodologies for automation to accelerate auditing processes to improve Quality, Safety, Privacy Risk Management, and Efficiency.
FlightPath and NeuroDNA: Creating a new interoperability standard for the evaluation of neurocognitive impairment
- Adam Cross, MD, OSF HealthCare
- Inki Kim, PhD, University of Illinois Urbana-Champaign
- Jessica Saw, MD, University of Illinois Urbana-Champaign
- Shenlong Wang, PhD, University of Illinois Urbana-Champaign
The current traumatic brain injury testing batteries are primarily paper-based, dependent on language and education, suffer from learning bias, and must be administered by a healthcare professional. This project seeks to address these limitations by developing a new interoperability standard for NCI based on an individual’s ability to track an object within a mixed reality (MR) space and will first test this paradigm as a novel method for the detection and characterization of concussion.
High-impact nurse education and care planning with multiuser, multi-agent virtual reality through a digital-twin ICU
- Inki Kim, PhD, University of Illinois Urbana-Champaign
- Paul Jeziorczak, MD, OSF HealthCare
- Celeste Schultz, PhD, RN-BC, CPNP-PC, University of Illinois Chicago
- Samantha Bothwell, MSN, RN, University of Illinois Chicago
Our goal for this project is to establish a new paradigm for nursing education and patient care in intensive care units (ICUs) using a novel digital-twin (DT) platform implemented for multiuser and multi-agent interactions in a virtual world. The resultant DT platform for nursing care in an ICU and new DT-based frameworks for nurse education and care management will provide the first multiuser, multi-agent simulation solution in healthcare to objectively capture, analyze, and understand the dynamic complexity of the ICU work systems.
Telerehabilitation of stroke patients through an adaptive multirobot architecture
- Dusan Stipanovic, PhD, University of Illinois Urbana-Champaign
- Anne Horowitz, OTR/L, OSF HealthCare
- Harris Nisar, University of Illinois Urbana-Champaign
A tele-rehabilitation framework is proposed in this project to facilitate home-based rehabilitation for stoke patients. Both the therapist and the patient would be provided with a robotic device and can perceive a virtual robot being operated in a virtual environment on a screen.
Re-engineering employee onboarding for health care in the digital age
- Lisa Barker, MD, OSF HealthCare
- George Heintz, MS, University of Illinois Urbana-Champaign
- Kyle Formella, MS, OSF HealthCare
Due to the “Great Resignation,” there is opportunity to approach the challenge more broadly by creating a framework that may be used to optimize roles from entry level to leadership. This tool will provide generalizable data collection and analysis that summarizes current state and provides value-based recommendations for improving onboarding process and proof of concept prototype onboarding program to target a high need areas across the Ministry.
Spring 2022 Project Awards
CliniPane: Developing a “Third Paradigm” clinical intelligence application
- Jonathan Handler, MD, FACEP, FAMIA, Senior Fellow for Innovation at OSF HealthCare
- Omar Elabd, MCS, University of Illinois Urbana-Champaign
- Roopa Foulger, OSF HealthCare
- Marlene Granda, University of Illinois Urbana-Champaign
This proposal aims to design and, if feasible, build “CliniPane”, a system to serve as a sort of clinical HUD on the user’s desktop. CliniPane would sit alongside the EHR, interact in real-time with it to garner clinical context and content, receive formatted information relevant to that context from a clinical intelligence engine, and non-interruptively push that information to the clinician via the application’s interface. Feedback provided by the user (volitional and potentially passive) through the interface will allow the application to continuously learn user preferences to optimize the user experience. In sum, the aim is clinical decision support that is ambient, anticipatorily pushed, needs-based designed, and continually learning.
Enhanced Focality of Transcranial Magnetic Stimulation Using an Ultrathin Wearable Metasurface for Treating Neurological Disorders
- Yang Zaho, PhD, University of Illinois Urbana-Champaign
- Yun-Sheng Chen, PhD, University of Illinois Urbana-Champaign
- Huan Huynh, MD, OSF HealthCare
The objective of this proposal is to demonstrate the feasibility of an innovative ultrathin metasurface device that is compatible with TMS to enhance the spatial resolution and control the focal distribution of the stimulating field. To attain the objective, the investigative team aims to:
- Design, fabricate, and test a metasurface device with a controllable surface impedance that reshapes the magnetic fields from the stimulator and thus to generate a refined focus; and
- Incorporate an integrable dual-modal photoacoustic/ultrasound imaging technology that images the brain oxygen saturation and blood flow, which are known to vary during TMS.
The dual-modal imaging technology can be seamlessly integrated and used together with electroencephalogram (EEG) as real-time physiological feedback for demonstrating the metasurface-enhanced focus during TMS.
VRtual Ed: A Virtual Three-Deminstional Educational Platform for Healthcare Students
- Avinash Gupta, PhD, University of Illinois Urbana-Champaign
- Lydia Lee, University of Illinois Urbana-Champaign
- John Shallat, MD, OSF HealthCare
- Maureen Matthews, APRN, OSF HealthCare
- Samantha Bothwell, MSN, RN, University of Illinois Chicago
- Celeste Schultz, PhD, University of Illinois Chicago
The proposed project will develop a virtual 3D interactive education platform using the Oculus Quest 2 as the head-mounted device (HMD) and Unity gaming software to create the hospital environment and interactive human-like digital avatars (created with the Reallusion Character Creator) integrated with artificial intelligence (Microsoft Language Understanding (LUIS), Google Speech Recognition) to replicate real-world interactions so that nursing students can gain the skills to become competent caregivers.
CAnPredict: An Algorithm for Improved Pancreatic Ductal Adenocarcinoma Detection
- Sonia Orcutt, MD, Department of Surgery, University of Illinois College of Medicine at Peoria
- Ravishanakar K. Iyer, PhD, University of Illinois Urbana-Champaign
- Christopher Gondi, PhD, University of Illinois Cancer Center
- Lusine Demirkhanyan, PhD, University of Illinois College of Medicine Peoria
- Andrew Darr, PhD, University of Illinois Urbana-Champaign
- James Weldy, OSF HealthCare
- Nathan Pritzker, MBA, OSF HealthCare
- Mosbah Aouad, University of Illinois Urbana-Champaign
The overarching goal of this proposal is to enhance the early detectability of PDAC and hence improve patient survival rate. To do so, our interdisciplinary research team, with expertise in basic and clinical science, machine learning (ML) and artificial intelligence (AI), will work together to develop a predictive diagnostic algorithm based on existing patient historical, multimodal data. While ML approaches have been applied to other disease models, there is no known example of successful prediction of disease onset (2-6) , and to the best of our knowledge, no such diagnostic tool is available for expedited prediction and detection of pancreatic cancer. This proposal seeks to bridge the gap in existing diagnostic models to achieve an early, accurate and a predictable outcome with the ultimate goal of improving patient survival. Successful completion of the work will not only be relevant to early pancreatic cancer diagnosis, but also establish new ways to perform diagnostic targeting of several other cryptic disease processes.
Incentivization of Health Care Initiatives with Cryptocurrency in a Zero-Knowledge System
- Jonathan Handler, MD, FACEP, FAMIA, Senior Fellow for Innovation at OSF HealthCare
- Tate Ralph, OSF HealthCare
- Wencui Han, PhD, University of Illinois Urbana-Champaign
- Andrew Miller, University of Illinois Urbana-Champaign
The current proposal seeks to implement a decentralized application utilizing blockchain technology to overcome this obstacle by employing the methodology of zero knowledge proof to provide double anonymity in the incentivization transaction. In this proposed architecture, patients would provide compliance data to the application and receive compensation based on the validity of their compliance. Health systems would provide the remuneration through an anonymous matching of the patient to a medical record within their electronic medical record. The patient’s ID would remain unknown, but the fact that the patient ID existed in the health system would be validated through implementation of zero knowledge proofs. The particular health system that matches to the patient ID is irrelevant to the value proposition for the patient and does not need to be exposed. Therefore, with both the patient remaining unidentified to the health system and the health system remaining unknown to the patient, double anonymity is achieved. With double anonymity, the claim that a health system is remunerating for compliance specifically to retain use of its services loses substance. No direct connection between the patient and health system can be identified. If successful, this proposal has the potential for impact, as direct compensation is likely the most effective tool for driving compliance and, therefore, improvements in individual and population health.
Toward Machine Learned Aortic Arch Measured Diameters
- Matthew Bramlet, M.D., Department of Pediatrics University of Illinois College of Medicine – Peoria; Advanced Imaging and Modeling Lab, Jump Simulation
- Brad Sutton, PhD, University of Illinois Urbana-Champaign
This project seeks to create a new tool that allows each institution to generate their own normative data which is relevant to their own institution, modality and methodology. In doing so, it will create confidence in clinical decisions based on local measures and experience. Current methods of generating pediatric normative data are cumbersome and time consuming. Automated methods of generating normative data have not yet been developed.
Development of a Chatbot for Delivering Long-Term Motivational Interviewing for Improving Exercise Adherence in Hemodialysis Patients
- Jessie Chin, PhD, School of Information Sciences, University of Illinois at Urbana-Champaign
- Suma Bhat, University of Illinois Urbana-Champaign
- Ben Pflederer, MD, OSF HealthCare
- Chung-Yi Chiu, PhD, University of Illinois Urbana-Champaign
- Ken Wilund, PhD, University of Illinois Urbana-Champaign
- Rehan Shah, University of Illinois Urbana-Champaign
Given the compromised health conditions and high self-management demands, hemodialysis patients have difficulty adhering to exercise programs. Motivational Interviewing (MI) is a counseling approach demonstrated to promote positive behavior change but is often impractical to conduct because of the administration costs (e.g., qualified professionals or frequency of sessions required). A possible alternative would be to develop an automated conversational agent to deliver the MI, but no prior research has demonstrated the feasibility or effectiveness of delivering long-term MI using natural conversations. The proposed study will bridge theories in behavioral sciences (e.g., MI and stages of behavioral change) and Natural Language Processing (NLP) to develop a long-term MI conversational agent, LogMintBot, for exercise adherence.
Pediatric Automated Intelligent Respiratory Support (PAIRS): Development of an Automatic Oxygen and Flow Weaning System for Pediatrics
- Keith Hanson, MD, PhD, Department of Pediatrics, University of Illinois College of Medicine Peoria
- Ramavarapu Sreenivas, PhD, University of Illinois Urbana-Champaign
- Adam Cross, MD, OSF HealthCare
- Roopa Foulger, OSF HealthCare
- Jonathan Gehlbach, MD, OSF HealthCare
The Pediatric Automated Intelligent Respiratory Support (PAIRS) system is an automated weaning system that will meet this need. The system will take into account all of a patient’s vital signs and adjust respiratory support as the patient is recovering. The software of the PAIRS system connects to and manipulates a modified Heated high flow nasal cannula device so that it can titrate both the FiO2 and the flow rate independently. This system can wean more frequently, and with less subjectivity, compared to human providers, whose assessments represent only single points in time and are biased based on experience and other factors. In contrast to typical clinical practice, the PAIRS system provides continuous patient assessment and real time intervention. The PAIRS system is hypothesized to improve care and decrease length of stay for pediatric patients who require respiratory support. The current proposal is to develop a prototype system with all the key components and test it in a simulated environment. Future work will include incorporating machine learning to improve the system logic and testing in the clinical environment.
Development of a pneumothorax computational model toward lung metastasis visualization and modeling
- Matthew Bramlet, M.D., Department of Pediatrics University of Illinois College of Medicine – Peoria; Advanced Imaging and Modeling Lab, Jump Simulation
- Brad Sutton, PhD, University of Illinois Urbana-Champaign
- Dan Robertson, MD, OSF HealthCare
- Alexa Waltz, OSF HealthCare
- Olivia Bryan, OSF HealthCare
This project, seeks to build on the utility of virtual reality based 3D modeling of pre-surgical anatomy by expanding beyond our previous work with congenital heart disease and large tumor resection which have already been fully implemented within the Children’s Hospital of Illinois (CHOI). At the core of this impact is the ability to recreate the surgical field in full 3D prior to the actual procedure allowing the surgeon to embed a more accurate mental representation of the surgical field in their mind prior to surgery. The more accurate 3D mental representation allows for more accurate surgical planning and decreases time under anesthesia “getting oriented” to the surgical anatomy as well as freeing working memory that would otherwise be spent aligning the reality of the surgical field to the 2D image surrogates of the anatomy. Surgical 3D localization of lung metastases is very difficult and affords much opportunity for direct translational patient impact. While 3D segmentation and modeling of lung lesions is possible within CHOI’s existing capability, the 3D surgical localization changes significantly with any deflation of the lung. This project looks to bridge the gap between existing capability and computational modeling of 3D localization in a deflated lung toward improved 3D mental representation of actual deflated surgical lung lesions.
Koopman framework for detecting mental health changes in multimodal wearable data
- Manuel E. Hernandez, PhD, College of Applied Health Sciences, University of Illinois at Urbana-Champaign
- Jean Clore, PhD, University of Illinois Urbana-Champaign
- Richard Sowers, PhD, University of Illinois Urbana-Champaign
- Elizabeth Hsiao-Wecksler, PhD, University of Illinois Urbana-Champaign
This interdisciplinary project aims to intuitively and intelligently collect, sense, connect, analyze and interpret wearable data from multimodal sensor systems to enable discovery of mental health symptoms, such as anxiety, and optimize health in adults. This objective will combine development of an ML/AI framework for detecting and predicting short-term and long-term changes in state anxiety, with the use of multimodal sensors to collect electrophysiological, acoustic, and/or kinematic measurements, and well-established psychosocial paradigms. Excessive anxiety has been shown to have detrimental effects on physical and cognitive performance. While self-reported measures have been used as a gold standard for evaluating anxiety, they are not feasible for continuous monitoring of anxiety and are unable to provide a measure of real-time, event-contingent changes in anxiety. Remote monitoring tools can provide objective and continuous monitoring and prediction of anxiety in vulnerable populations. However, biosensor integration with data analytic approaches are lacking. Collected physiological data often exhibit multiple complicating factors, e.g. noise or sparse and irregular sampling, due to biofouling of sensing platforms and/or complexity of physiological data. Fundamental knowledge connecting sensors with existing models and/or ML is missing.
Advanced Auscultation Audio Algorithmic Analysis (a5)
- Adam Cross, MD, FAAP, Clinical Research Informaticist, OSF HealthCare
- Jennifer Amos, PhD, University of Illinois Urbana-Champaign
- Eliot Bethke, University of Illinois Urbana-Champaign
The proposed project will focus on advanced feature extraction and processing to improve analytical performance to enable end-to-end explainable output and avoid the “black box” problem so prevalent among current models in the literature. The project will use the publicly available ICBHI 2017 dataset of 920 lung audio recordings from 126 subjects. We will implement raters trained in auscultation to label adventitious lung sounds as well as inhalation, exhalation, and other notable lung sounds in our dataset, then use this data to build and test the algorithm. The output of the model will be critically reviewed by human experts, taking careful note of lung sound types and diagnoses that are challenging to label for human raters and for the algorithm.
