2023 Spring Seminar Series
Portable and Integrative Biomechanics Labs for Precision Rehabilitation
Date: May 23, 2023 | 1:00PM-2:00PM, on Zoom
Speakers: Eni Halilaj, PhD
Abstract:
Musculoskeletal conditions are the leading cause of disability worldwide, costing the United States’ economy $950 billion per year in direct and indirect costs. In this talk, I will share our work on key challenges toward precision rehabilitation for musculoskeletal injuries and diseases. I will first describe how we are combining deep learning with physics-based modeling to democratize motion capture for all researchers, clinicians, and patients, making it accessible from personal smartphones and inexpensive wearable sensors. These portable tools will strengthen the feedback loop between research and clinical practice, enabling large-scale research-grade data to be collected outside of specialized gait laboratories. I will then talk about the importance of understanding interactions between mechanical loading (e.g., in response to exercise, injury, assistive wearables) and biological factors (i.e., deep patient phenotyping) when personalizing rehabilitation protocols and assistive devices. Our ultimate goal is to build smart rehabilitation technologies that can capture meaningful biomechanical outcomes in natural environments, reason about what these biomechanics mean for a particular patient and guide the delivery of personalized feedback in real time. Toward this vision, we are studying flexible sensors that can be embedded into smart garments for continuous monitoring and haptic feedback. In this last part of my talk, I will share our collaborative work on characterizing capacitive sensing as a biomechanics-monitoring tool.
Minding the Gap: Why Realizing the Potential of Social Determinants of Health is Important for Biomedical Research in Addressing Mental Health Disparities and Youth Suicide
Date: May 9, 2023 | 1:00PM-2:00PM, on Zoom
Speakers: Yunyu Xiao, PhD
Abstract:
In this talk, we will dive into the alarming public health crisis of suicide, a pervasive issue that disproportionately impacts marginalized communities. We will embark on a journey to uncover the hidden epidemiological patterns of health disparities and suicide, and unveil the powerful Social Determinants of Health (SDoH) models rooted in social sciences.
As we explore the SDoH framework, you will learn how its integration into marketing and biomedical research is critical to promoting mental health equity and fostering change. We'll delve into various cutting-edge methodologies for identifying, studying, and addressing SDoH, opening up a world of possibilities for revolutionizing mental health care.
Finally, we will reveal the remarkable results achieved through interdisciplinary collaboration, harnessing the power of electronic health records and large-scale population-based studies. Together, we will unlock the potential of SDoH to transform the landscape of mental health equity, and work towards a future where everyone has the opportunity to thrive.
Unlocking the Secrets of Early Brain Development: Advancements in Neuroimaging, AI, and Multi-Omics
Date: April 25, 2023 | 1:00PM-2:00PM, on Zoom
Speakers: Yun Wang, PhD
Abstract:
Studying early brain development is crucial for understanding the foundations of cognitive and emotional functioning, and for identifying potential interventions to promote mental health. The primary research objective is to unravel the complexities of early neurodevelopment and identify key factors contributing to risk and resilience for mental illness. The laboratory harnesses artificial intelligence (AI) to enhance medical imaging analysis in fetuses and infants and employs a multi-omics approach to investigate intricate relationships between maternal factors, multi-omics data, brain development, and behavioral outcomes, such as social deficits, emotional dysregulation, and other developmental or psychiatric disorders. To address the shortcomings of traditional neuroimaging techniques, the lab is developing an advanced, accurate, and scalable AI-driven analysis platform specifically designed for fetal and infant brain imaging. Utilizing AI's capabilities in multi-omics, the interconnected factors influencing multi-omics data and their subsequent effects on brain development and behavior are explored. This pioneering approach will lay the foundation for novel treatments and interventions, fostering healthy development and reducing the prevalence of neurodevelopmental and psychiatric disorders. In essence, the lab's work focuses on revolutionizing fetal and infant neuroimaging through AI integration and in-depth multi-omics data analysis, providing a comprehensive understanding of early brain development, the dynamic interplay of key determinants, and their implications for behavioral outcomes and disorders.
Enhancing Diagnostic Precision through AI-Driven Imaging Signatures
Date: April 18, 2023 | 12:00PM-1:00PM, in BMI calssroom 4004 or on Zoom
Speakers: Saima Rathore, PhD
Abstract:
As modern medical imaging becomes increasingly multi-parametric, interpreting these images in neurodegenerative and oncologic diseases has become more complex. This underscores the importance of developing advanced methods that condense the vast amount of information these images convey into a concise, comprehensive, and easily interpretable panel of measurements that provide highly informative insights. Artificial Intelligence (AI) methods have revolutionized the integration of diverse imaging data, revealing unique and individualized imaging patterns with diagnostic and prognostic value. They have enabled researchers to move beyond group comparisons and instead focus on identifying imaging biomarkers that offer personalized value. This talk will review several research directions around this topic, emphasizing the use of AI-based advances in imaging-based biomarkers for faster, safer, and more effective implementation of biomarker-based classification, risk assessment, diagnosis, and prognosis in neurodegenerative and oncologic diseases. Additionally, this talk will discuss how AI-based methods have been instrumental in breaking down broader diagnostic categories into more distinct subgroups and how they can non-invasively estimate molecular characteristics and predict therapy responses. The discussion will also demonstrate how introducing precise AI-based biomarkers for clinical outcomes is enhancing precision diagnostics and paving the way for a revolutionary approach to precision medicine that can deliver more personalized and effective patient care.
Multi-domain data integration for precision health
Date: March 21, 2023 | 1:00PM-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 | 12:00PM-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 | 12:00PM-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 | 1:00PM-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 | 12:00PM-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.
Click here to view previous events.