Penn State National Science Foundation Center for Health Organization Transformation
(Penn State CHOT) - An Industry-University Cooperative Research Center
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In the news: Christopher DeFlitch discusses critical topics about COVID-19
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Christopher DeFlitch, chief medical information officer at Penn State Health and CHOT co-principal
investigator
, discussed advances in telehealth the amid ongoing COVID-19 pandemic. In these unprecedented days of "social distancing," telehealth technology, including Penn State Health OnDemand, is bridging the physical gap between patients and providers who need to stay connected and safe. Responding to the expected surge of patients needing COVID-19 testing, the Penn State TeleHealth team sprang into action to set up free, virtual coronavirus screening through its Penn State Health OnDemand platform. Patients can stay at home and be evaluated through services provided by more than 100 Penn State Health providers. Available around the clock, the coronavirus screening allows patients to report symptoms and, if warranted, routes patients to a Penn State Health drive-through COVID-19 testing site or to a care location. Read more. Also, check out PennLive's Coronavirus Q & A on Facebook Live, featuring DeFlitch.
DeFlitch and
Peter Dillon, executive vice president and chief clinical officer at Penn State Health,
each discussed the important work front line workers are doing and the importance of keeping them safe in local news.
Watch the interview here.
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Manufacturing and Sterilization for COVID-19 (MASC) Initiative
In response to the ongoing pandemic, the MASC Initiative was launched at Penn State in March 2020. With more than 350 researchers and counting contributing their expertise, the initiative is focused on designing and delivering rapidly scalable solutions and generating tangible impact, particularly within the commonwealth of Pennsylvania. Recently, they created a dashboard showing the supply chain relevant to COVID-19 in Pennsylvania. View the dashboard.
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Penn State CHOT Research Highlights
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Sensor-based virtual reality for clinical decision support in the assessment of mental disorders
Two CHOT scholars, Bryant Niederriter and Alice Rong, along with CHOT faculty adviser Faisal Aqlan, developed a virtual-reality based method for mental disorders. Recent reports show that 1 in 4 families has at least one member with a mental disorder. The current diagnosis in psychiatry is based on clinical interviews and questionnaires, which are subjective and can lead to recalls and interviewer biases. Virtual reality (VR) is a new immersive technology that provides an unprecedented opportunity to measure one's feedback in the virtual environment. In health care, VR shows strong potential to improve decision making and help patients to better connect with reality, cope with pain, and overcome mental disorders such as anxiety and depression. This study integrates sensing technology (i.e., eye tracking) with VR simulation of health care environments to improve clinical judgment and assessment of mental disorders. Traditional scenario-based patient simulations are used as a basis for the development of VR modules. Data collected via sensing technology are utilized to develop analytical models for predicting the risk of mental illness. Moreover, artificial intelligence (AI) tools for VR-based health care training help students learn faster and make smarter decisions. This research helps contribute to improved population health by developing new methods for promoting health and effectively predicting and treating mental disorders.
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Mosaic privacy-preserving mechanisms for health care analytics
Alexander Krall, a CHOT scholar, developed a novel cyber security method to mitigate model inversion cyber attacks.
The Internet of Things (IoT) has propelled the evolution of medical sensing technologies to greater heights. Thus, traditional health systems have been transformed into new data-rich environments. This provides an unprecedented opportunity to develop new analytical methods and tools toward a new paradigm of smart and interconnected health systems. Nevertheless, there are risks pertinent to increasing levels of system connectivity and data accessibility. Cyber attacks become more prevalent and complex, leading to greater likelihood of data breaches. These events bring sudden disruptions to routine operations and cause the loss of billions of dollars. Adversaries often attempt to leverage models to learn a target's sensitive attributes or extrapolate its inclusion within a database as described in the figure below.
As health care systems are critical to improving the wellbeing of our society, there is an urgent need to protect the privacy of patients and minimize the risk of model inversion attacks. This paper presents a new approach, named Mosaic Gradient Perturbation (MGP), to preserve privacy in the framework of predictive modeling, which meets the requirement of differential privacy while mitigating the risk of model inversion. MGP is flexible in fine-tuning the trade-offs between model performance and attack accuracy while being highly scalable for large-scale computing. Experimental results show that the proposed MGP method improves upon traditional gradient perturbation to mitigate the risk of model inversion while offering greater preservation of model accuracy. The MGP technique shows strong potential to circumvent paramount costs due to privacy breaches while maintaining the quality of existing decision-support systems, thereby ushering in a privacy-preserving smart health system.
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Heterogeneous recurrence analysis of spatial data
Penn State CHOT researchers
Hui Yang, Cheng-Bang Chen, and Soundar Kumara
developed a new method to analyze images to improve health care and manufacturing. Patterns appear in both natural and human made systems, but they can be difficult for humans to recognize and analyze, especially in dynamic systems like the human heart or factory machines. The researchers focused on understanding patterns in nonlinear, dynamic systems, as these intricate systems are challenging to analyze due to their nature - they fluctuate over multiple dimensions, such as space and time, and are near impossible to understand via human observation. This algorithm analyzes heterogeneous types of recurrences in the spatial data, which allows researchers to build a bridge between biological patterns, like in human anatomy, and man-made patterns, like in manufacturing. The algorithm has broad implications for medical applications such as monitoring organ function, analyzing cancer images, and detecting organ dysfunction over time. This work is published in the
American Institute of Physics Chaos Journal.
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Spring 2020 CHOT IAB Meeting
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Highlights in Spring 2020 CHOT IAB conference call
This year, the Spring 2020 CHOT IAB meeting went online in response to the spread of COVID-19. The conference call focused on COVID-19, and speakers from various fields shared their ideas. The conference call was recorded and available online. Check out the video embedded in this article for more information. It has a lot of important conversations and brainstorming that will provide research opportunities.
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IEEE Journal of Biomedical and Health Informatics (JBHI) special issue
Submission Deadline: Continuous up to December 31, 2020
IEEE JBHI is now accepting submission on a special issue: AI-driven informatics, sensing, imaging and big data analytics for fighting the COVID-19 pandemic.
This Special Issue aims (1) to encourage the stakeholders relating to COVID-19 to share data source, data harmonization, and tools, which can speed up COVID-19 research for years to come; (2) to inspire new informatics method development for rapid testing of virus in humans; (3) to present advanced informatics solutions that utilize machine learning and artificial intelligence methods such as deep learning to analyze COVID-19 data for diagnosis, treatment, and prognosis; (4) to develop computational models and tools to track virus propagation and recurrence; and (5) to model outbreaks for policy makers for better decision making. Informatics goals include data harmonization, data quality control, multi-modality data integration, advanced analysis pipeline such as deep learning, causal inference, real-time decision making, and interpretable models.
Researchers who are using informatics to address COVID-19 issues are encouraged to submit high-quality data and unpublished work.
The submitted manuscripts will be processed through a fast track procedure, and the time from submission to first decision will be limited to 15 days.
Topics of interest include, but are not limited to, the following:
- Collection, harmonization, sharing, and visualization of COVID-19 related data
- AI-driven exploration of susceptibility and infection in humans
- Modeling of virus propagation, recurrence, and virulence from epidemiological observations
- AI-driven medical imaging (including chest X-ray and CT) analysis for COVID-19 detection
- AI-driven histopathology analysis for COVID-19 diagnosis
- Bioinformatics for COVID-19 subtype rational drug design
- ML-based treatment evaluation and outcome prediction
- AI-based care pathways planning for comorbid patients
- Deep Learning for COVID-19 treatment and prognosis
- Sensor informatics for monitoring COVID-19 infected patients at home or in ICU
- Informatics-driven rapid testing of the virus in humans
- In silico modeling of clinical trials in COVID-19 drug and vaccine development
- Big Data-enabled Citizen-Mediated Public Health Policy making
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As event guidance concerning the COVID-19 pandemic continues to evolve, please note that these events may be rescheduled or moved to a virtual venue. Please check in with the specific event planners for the latest information.
July 20-24, 2020
The 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2020) will go virtual. This is the message from active EMBS President Shankar Subramaniam: "EMBS, which is a society at the interface of engineering and medicine, can play a significant role in addressing several challenges associated with treating the epidemic, and the role of engineering in dealing with this crisis cannot be overemphasized." In the coming weeks, the organizing committee will be reaching out to all conference stakeholders (authors, speakers, exhibitors, etc.) with the expectations related to this new conference framework.
Click here
for more information.
IEEE CASE 2020
August 20-24, 2020
The in-person gathering of CASE 2020, scheduled to be held August 20-24, 2020 in Hong Kong has been cancelled. CASE 2020 will now be held as a virtual conference, to be held August 20-24, 2020. The IEEE CASE is the flagship automation conference of the IEEE Robotics and Automation Society and constitutes the primary forum for cross-industry and multidisciplinary research in automation. Its goal is to provide a broad coverage and dissemination of fundamental research in automation among researchers, academics, and practitioners. The theme of the conference is
Automation Analytics.
Click here for more information.
IISE Annual Conference 2020
October 31 - November 3
New Orleans, Louisiana
In response to the COVID-19 pandemic, the IISE Annual Conference & Expo has been rescheduled from May 30 - June 2 to October 31 - November 3. The Institute of Industrial and Systems Engineers (IISE) will host the IISE Annual Conference & Expo in New Orleans, Louisiana at the Hyatt Regency New Orleans. Leaders in the field will join up-and-comers and students to network, gather new ideas, and learn about innovative tools and techniques - resulting in lasting career connections. Please check the revised schedule and new registration dates. Click here for more information.
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Check out the CHOT website for information about our new projects and exciting activities.
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If you have any exciting news that you would like to feature in our CHOT newsletter, please submit it to Haedong Kim, newsletter editor, at
[email protected].
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