2023 Events
2023 Spring Seminar Series
Multi-domain data integration for precision health
Date: March 21, 2023 | 1:00-2:00PM, on Zoom
Speakers: Xiao Li, PhD
Abstract:
The large amount of biomedical data derived from molecular profiling, wearable sensors, and electronic health records are rapidly transforming our healthcare systems. Our lab focuses on integrating these multi-domain data to promote precision health. Recently, we conducted a secondary analysis of dietary, metabolic, and molecular data collected from 609 participants before, during, and after a 1-year weight-loss intervention with either a healthy low-carbohydrate (HLC) or a healthy low-fat (HLF) diet. Through systematic analysis of multidomain datasets, we identified dietary, molecular, and metabolic factors, common or unique to the HLC and HLF diets, providing a roadmap for developing individualized weight-loss strategies. As a widely deployed technology that has been used by tens of millions of people worldwide, the utility of wearable technologies in health management has been another major focus of the lab. In the current pandemic, we investigate the use of wearable devices for the early detection of COVID-19 in a retrospective manner and also present an approach for using wearable device-detected physiological parameters for real-time health monitoring and surveillance. To meet the big data challenges, we further developed the Personal Health Dashboard (PHD), which utilizes state-of-the-art security and scalability technologies to provide an end-to-end solution for big biomedical data analytics. Supported by PHD framework, we are currently investigating the potential of wearable technologies in precisely predicting and managing human diseases and health.
Wearable Sensors and Artificial Intelligence Technologies for Chronic and Infectious Disease Monitoring
Date: March 14, 2023 | 12:00-1:00PM, BMI Classroom 4004 and on Zoom
Speakers: MD Mobashir Hasan Shandhi, PhD
Abstract:
The majority of the health care costs related to the treatment of chronic and infectious diseases are attributed to direct care costs (e.g., hospital admissions and readmissions). The prevalence of chronic diseases and associated costs in the United States is growing at an alarming pace. The COVID-19 pandemic has further impacted the health of high-risk individuals by increasing the likelihood of more severe illness for those with underlying health conditions and associated healthcare costs. There have been ample efforts from researchers and clinicians to develop remote healthcare systems and wearable devices to manage patients with chronic and infectious diseases in home settings, which has reduced the burden on inpatient care facilities and gained further momentum during the COVID-19 pandemic. Yet, there is a lack of reliable wearable devices that can provide clinically acceptable information to healthcare professionals, as well as a lack of emphasis on validating wearable and artificial intelligence technologies in representative populations to enable a reliable and equitable remote health management system. This talk will present the challenges and potential solutions for developing tools (i.e., wearable sensors and computational algorithms) for reliable and equitable remote patient monitoring systems for chronic and infectious diseases. More specifically, the presenter will share the development and validation of wearable sensors and computational algorithms for cardiovascular health monitoring, particularly for patients with heart failure, and the development of an intelligent allocation method of diagnostic testing for COVID-19 using commercial wearables.
Integrating AI with Care: Lessons Learned in Public Health
Date: March 7, 2023 | 12:00-1:00PM, BMI Classroom 4004 and on Zoom
Speakers: Azra Ismail
Abstract:
There has been growing interest in applying Artificial Intelligence (AI) in public health, motivated by scarce and unequal access to care in communities globally. Many of these efforts promise improved health outcomes but also risk amplifying existing inequities. This calls for the human-centered design of AI to ensure alignment with the needs and experiences of communities and health workers targeted. This talk presents lessons learned from AI integration in public health, in the context of maternal and child care delivery in India. I will share three studies on the integration of machine learning and conversational agents with existing (1) data flows and practices, (2) organizational structures and goals, and (3) care workflows. My work centers care to draw attention to how AI is embedded in a broader ecology with multiple stakeholders, and tensions that may arise when targeting equity with AI. Finally, I chart a path forward for greater human-AI collaboration in healthcare, to improve care experiences for marginalized communities and workers.
Leveraging Machine Learning and Clinical Decision Support for Delirium Prediction
Date: March 2, 2023 | 1:00-2:00PM
Speakers: Siru Liu, PhD
Abstract:
This talk will introduce a three-stage process to develop and implement a clinical decision support tool to predict new onset delirium in hospitalized adult patients. The tool used explainable machine learning and deep learning with an AUC of 0.927 and 0.952, respectively. In the second stage, the tool was developed through a user-centered design and evaluated for usability. In the final stage, the tool was implemented on an EHR vendor and evaluated through clinician interviews and questionnaires. The tool has the potential to reduce delirium by identifying high-risk patients, reducing the frequency of neurological assessments for low-risk patients, and assisting in discharge or transfer.
Multimodal Screening Algorithms for Mitigating the Burden of Delayed Start of Care and the Risk of Negative Outcomes
Date: February 21, 2023 | 12:00-1:00PM
Speakers: Maryam Zolnoori, PhD
Abstract:
Delayed start of care is associated with a significant burden on patients, caregivers, and healthcare systems. Recognizing this burden, the National Institutes of Health introduced initiatives (e.g., screening algorithm) built on novel data science methods and data streams generated during routine clinical encounters as a solution with significant potential to improve the quality and safety of healthcare services and reduce healthcare costs. My program of research is dedicated to mitigating the burden of the delayed start of care through the use of multiple data science methods that take advantage of discrete data points from multiple sources (e.g., speech data, free-text clinical notes) for early identification of patients at risk of health deterioration and negative outcomes. In this talk, I present my journey in developing novel informatics solutions for mitigating delayed start of care, with a special focus on patients with mental health problems.
2022/2023 Winter Seminar Series
Contrasting Learning of Electrodermal Activity Representations for Stress Detection
Date: February 14, 2023 | 1:00-2:00pm
Speakers: Robert Lewis, PhD Candidate and Katie Matton, PhD Candidate
Abstract:
Electrodermal activity (EDA) is a biosignal that contains valuable information for health monitoring. Changes in EDA are related to sympathetic nervous system activity, which varies with changing levels of psychological arousal. Thus, a leading application of EDA is the estimation of stress and other related mental health constructs. However, wearable EDA measurements are challenging to analyze as the data tend to be noisy and sparsely labelled. In this talk, we will discuss our on-going work to address these challenges using contrastive learning approaches that are tailored to account for the specific properties of the EDA signal.
From Conventional Ophthalmology to Emerging Digital Ophthalmology
Date: December 13, 2022 | 1:00-2:00pm
Speakers: Siamak Yousefi, PhD
Abstract:
Large and complex imaging, visual field, and genetic datasets are becoming increasingly available to the eye and vision research community. However, analyzing such complex data is not trivial and typically requires advanced data mining and machine learning techniques to extract useful knowledge and making sense of findings. In this talk, I will briefly review applications of artificial intelligence models to automatically detect some of the ocular diseases. I will explore early and emerging artificial-intelligence-based models for forecasting, screening, diagnosis, and monitoring several retinal and corneal conditions with an emphasis on glaucoma as the second leading cause of blindness worldwide.
Toward Ubiquitous Intelligent Systems for Managing Neurological Disorders
Date: December 6, 2022 | 1:00-2:00pm
Speakers: Hyeokhyen Kwon, PhD
Abstract:
Neurodegenerative disorders, such as Alzheimer's disease or Parkinson's disease, negatively impact both patients and caregivers, leading to emotionally, financially, and physically exhausting experiences. In order to promote patients' active and independent management of these diseases, early detection and continuous monitoring of disease progression are crucial. The advancements in hardware and software frameworks for edge devices, along with AI/ML techniques utilizing on- and off-body sensors, have made it possible to continuously and passively quantify patients' physical, mental, and social behavior in both clinical and non-clinical settings. Through the application of ubiquitous intelligent systems, I will discuss the grand challenges in patient care and the latest advances in monitoring neurodegenerative disorders.
Wearable Sensors for Chronic and Infectious Disease Monitoring
Date: November 15, 2022 | 12:00-1:00pm
Speakers: Md Mabashir Hasan Shandhi, PhD
Abstract:
The majority of the health care costs related to the treatment of chronic and infectious diseases are attributed to direct care costs (e.g., hospital admissions and readmissions). The prevalence of chronic diseases and associated costs in the United States is growing at an alarming pace. The COVID-19 pandemic has further impacted the health of high-risk individuals by increasing the likelihood of more severe illness for those with underlying health conditions and associated healthcare costs. There have been ample efforts from researchers and clinicians to develop remote healthcare systems and wearable devices to manage patients with chronic and infectious diseases in home settings, which has reduced the burden on inpatient care facilities and gained further momentum during the COVID-19 pandemic. Yet, there is a lack of reliable wearable devices that can provide clinically acceptable information to healthcare professionals, as well as a lack of emphasis on validating technologies in representative populations to enable a reliable and equitable remote health management system. This talk will present the challenges and potential solutions for developing tools (i.e., wearable sensors and computational algorithms) for reliable and equitable remote patient monitoring systems for chronic and infectious diseases. More specifically, the presenter will share the development and validation of wearable sensors and computational algorithms for cardiovascular health monitoring, particularly for patients with heart failure, and the development of an intelligent allocation method of diagnostic testing for COVID-19 using commercial wearables.
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