Professor of Medicine, Institute for Informatics (I2)
Director, Center for Population Health Informatics at I2,
Washington University in St. Louis, School of Medicine
TITLE: Building Trust in AI for Improving Health Outcomes
ABSTRACT: Given recent digital transformation in healthcare, it is imperative to consider the appropriate application of artificial intelligence (AI). From the perspective of an epidemiologist, this talk will explore the right tasks, the right data, the right evidence standard, and the right approaches for integrating AI into clinical care.
BIO: Dr. Foraker is the Director of the Center for Population Health Informatics at the Institute for Informatics, Interim Director of the Division of Biostatistics, and a Professor of Medicine at Washington University in St. Louis. Dr. Foraker also serves as Director of the Public Health Data and Training Center for the Institute for Public Health and Director of the Center for Administrative Data Research. Dr. Foraker specializes in the design of population-based studies and the integration of electronic health record (EHR) data with socioeconomic indicators as well as the use of synthetic data for research. Her recent research has focused on the application of clinical decision support – embedded in the EHR – to complement risk scoring in primary care, cardiology, and oncology.
Max Topaz, PhD, RN, MA, FAAN
Director, Center for Population Health Informatics at I2,
Washington University in St. Louis, School of Medicine
TITLE: Building Trust in AI for Improving Health Outcomes
ABSTRACT: Given recent digital transformation in healthcare, it is imperative to consider the appropriate application of artificial intelligence (AI). From the perspective of an epidemiologist, this talk will explore the right tasks, the right data, the right evidence standard, and the right approaches for integrating AI into clinical care.
BIO: Dr. Foraker is the Director of the Center for Population Health Informatics at the Institute for Informatics, Interim Director of the Division of Biostatistics, and a Professor of Medicine at Washington University in St. Louis. Dr. Foraker also serves as Director of the Public Health Data and Training Center for the Institute for Public Health and Director of the Center for Administrative Data Research. Dr. Foraker specializes in the design of population-based studies and the integration of electronic health record (EHR) data with socioeconomic indicators as well as the use of synthetic data for research. Her recent research has focused on the application of clinical decision support – embedded in the EHR – to complement risk scoring in primary care, cardiology, and oncology.
Max Topaz, PhD, RN, MA, FAAN
Elizabeth Standish Gill Associate Professor of Nursing, Columbia University School of Nursing,
Columbia University Data Science Institute
TITLE: Artificial Intelligence in Patient’s Home: Current Trends
and Future Directions
ABSTRACT: Artificial intelligence technologies are reshaping the way clinical care is provided internationally. This presentation will overview several current trends of artificial intelligence in the patient home with concrete examples of studies currently being conducted to develop and implement a range of technologies in home healthcare settings. The presentation will review examples of automated clinical decision support that helps to identify high-risk patients who should be prioritized for the first home healthcare nursing visits. In addition, the presentation will describe several ways in which deteriorating patients can be identified automatically, using routinely collected home healthcare data. Finally, the presentation will describe cutting-edge speech recognition work to detect patient deterioration, cognitive impairment, and other negative outcomes.
BIO: Dr. Maxim (Max) Topaz PhD, RN, MA is the Elizabeth Standish Gill Associate Professor of Nursing at the Columbia University School of Nursing. He is also affiliated with Columbia University Data Science Institute and the Center for Home Care Policy & Research at the Visiting Nurse Service of New York. His research focusses on artificial intelligence and he finds innovative ways to use the most recent technological breakthroughs, like text or data mining, to improve human health. Dr. Topaz’s research moto is “Data for good”. Dr. Topaz is one of the pioneers in applying natural language processing on data generated by nurses. His current work focusses on developing artificial intelligence solutions to advance clinical decision making. In the past, Dr. Topaz was involved with health policy (national and international levels), leadership (e.g. Co-founder and Chair of the Students and Emerging Professionals Working Group of the International Medical Informatics Association) and health entrepreneurship. Dr. Topaz's clinical experience is in internal and urgent medicine. He earned his PhD degree as a Fulbright Fellow at the University of Pennsylvania and his Masters and Bachelors degrees from the University of Haifa, Israel. He completed a postdoctoral fellowship at the Harvard Medical School and Brigham Women's Hospital. He served as a Senior Lecturer at the School of Nursing, University of Haifa (Israel) where he was heading a Health Information Technology Lab. He published more than sixty articles on topics related to health informatics and received numerous prestigious awards for his work.
Morgan Levine, PhD
Columbia University Data Science Institute
TITLE: Artificial Intelligence in Patient’s Home: Current Trends
and Future Directions
ABSTRACT: Artificial intelligence technologies are reshaping the way clinical care is provided internationally. This presentation will overview several current trends of artificial intelligence in the patient home with concrete examples of studies currently being conducted to develop and implement a range of technologies in home healthcare settings. The presentation will review examples of automated clinical decision support that helps to identify high-risk patients who should be prioritized for the first home healthcare nursing visits. In addition, the presentation will describe several ways in which deteriorating patients can be identified automatically, using routinely collected home healthcare data. Finally, the presentation will describe cutting-edge speech recognition work to detect patient deterioration, cognitive impairment, and other negative outcomes.
BIO: Dr. Maxim (Max) Topaz PhD, RN, MA is the Elizabeth Standish Gill Associate Professor of Nursing at the Columbia University School of Nursing. He is also affiliated with Columbia University Data Science Institute and the Center for Home Care Policy & Research at the Visiting Nurse Service of New York. His research focusses on artificial intelligence and he finds innovative ways to use the most recent technological breakthroughs, like text or data mining, to improve human health. Dr. Topaz’s research moto is “Data for good”. Dr. Topaz is one of the pioneers in applying natural language processing on data generated by nurses. His current work focusses on developing artificial intelligence solutions to advance clinical decision making. In the past, Dr. Topaz was involved with health policy (national and international levels), leadership (e.g. Co-founder and Chair of the Students and Emerging Professionals Working Group of the International Medical Informatics Association) and health entrepreneurship. Dr. Topaz's clinical experience is in internal and urgent medicine. He earned his PhD degree as a Fulbright Fellow at the University of Pennsylvania and his Masters and Bachelors degrees from the University of Haifa, Israel. He completed a postdoctoral fellowship at the Harvard Medical School and Brigham Women's Hospital. He served as a Senior Lecturer at the School of Nursing, University of Haifa (Israel) where he was heading a Health Information Technology Lab. He published more than sixty articles on topics related to health informatics and received numerous prestigious awards for his work.
Morgan Levine, PhD
Founding Principal Investigator at Altos Labs
Formerly a ladder rank professor at Yale University
TITLE: Promises, Considerations, and Caveats for Quantifying
the biological aging process
ABSTRACT: Aging is considered the single biggest risk factor for most major chronic conditions, from cancer to Alzheimer’s disease. As such, scientists have speculated that identifying interventions to slow or even reverse aging in humans would have massive implications for public health. In addition to the difficulty of identifying such factors, another hurdle is in evaluating the efficacy of any potential intervention. Biological aging is a latent concept and there is no agreed upon method for estimating it. My talk will cover the underlying goals of why we need to develop robust estimates of biological aging; where the field currently stand—particularly in the context of using high-dimensional omics data—and what considerations and caveats need to be considered as we move forward.
BIO: Dr. Morgan Levine is a founding Principal Investigator at Altos Labs and formerly a ladder rank professor at Yale University. Levine is considered a leader in the biology of aging. Her work focuses on developing methods for quantifying complex latent phenomena, such as aging, health, and resilience. Her lab also combines computational and experimental approaches with the goal of modeling and programming the epigenetic systems underlying cellular states. Levine has received numerous awards for her work, including the Vincent Cristofalo Rising Star Award in Aging Research in 2021 and the Nathan Shock New Investigator Award in 2020.
Formerly a ladder rank professor at Yale University
TITLE: Promises, Considerations, and Caveats for Quantifying
the biological aging process
ABSTRACT: Aging is considered the single biggest risk factor for most major chronic conditions, from cancer to Alzheimer’s disease. As such, scientists have speculated that identifying interventions to slow or even reverse aging in humans would have massive implications for public health. In addition to the difficulty of identifying such factors, another hurdle is in evaluating the efficacy of any potential intervention. Biological aging is a latent concept and there is no agreed upon method for estimating it. My talk will cover the underlying goals of why we need to develop robust estimates of biological aging; where the field currently stand—particularly in the context of using high-dimensional omics data—and what considerations and caveats need to be considered as we move forward.
BIO: Dr. Morgan Levine is a founding Principal Investigator at Altos Labs and formerly a ladder rank professor at Yale University. Levine is considered a leader in the biology of aging. Her work focuses on developing methods for quantifying complex latent phenomena, such as aging, health, and resilience. Her lab also combines computational and experimental approaches with the goal of modeling and programming the epigenetic systems underlying cellular states. Levine has received numerous awards for her work, including the Vincent Cristofalo Rising Star Award in Aging Research in 2021 and the Nathan Shock New Investigator Award in 2020.