Applications of Machine Learning in Sickle Cell Disease

Earn 5.5 CME credits is for attending

Agenda

This Symposium is virtual and hosted on Zoom. It begins at 10:00am EST and ends at 4:00pm EST. The Symposium will be recorded and replay will be available for participants.

Times (EST) September 22, 2022 Speaker(s)
10:00 am – 10:10 am Welcome & Thanks Dr. Kenneth Ataga
10:10 am – 10:40 am Introduction to Machine Learning

Introduction to Machine Learning and Opportunities in Management of Sickle Cell Disease
Dr. Oguz Akbilgic
10:40 am – 11:10 am Utility of Machine Learning in Clinical Practice Dr. Robert Davis
11:10 am – 12:10 pm Machine Learning to Predict Pain Crisis

iHurt: Smart Pain Assessment System Employing Multi-Modal Behavioral and Physiologic Indicators

Dr. Amir Rahmani
12:10 pm – 12:40 pm Lunch
12:40 pm – 1:10 pm Machine Learning and Hospital Readmission Dr. Enrico Novelli
1:10 pm – 1:40 pm Machine Learning and Organ Failure

Machine Learning Prediction Model for Acute Organ Failure in critically ill SCD patients

Dr. Akram Mohammed
1:40 pm – 2:10 pm Machine Learning and Kidney Disease

Predicting Rapid Decline in Kidney Function in Patients with Sickle Cell Disease

Dr. Fatma Gunturkun
2:10 pm – 2:40 pm Machine Learning and Mortality/Heart Disease

A phenotypic risk score for predicting mortality in SCD

Dr. Vandana Sachdev
Dr. Colin Wu
Dr. Swee Lay Thein
2:40 pm – 3:10 pm Machine Learning and Acute Chest Syndrome

Early Detection of Acute Chest Syndrome Through Electronic Recording and Analysis of Auscultatory Percussion

Dr. Thomas J. Royston
3:10 pm – 3:40 pm Machine Learning and Biomarkers

Can machine learning utilize population-based blood function data to enable better preventative care strategies for people living with sickle cell disease? 

Dr. Patrick Hines

 

Applications of Machine Learning in Sickle Cell Disease

Supporting Sponsor

$1,000

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  • 5 minute speaking time during lunch break

Become a Member of the Sickle Cell Research Society of America.

Price: $375

Whether you attend every year or this will be your first, THE FOUNDATION FOR SICKLE CELL DISEASE RESEARCH’S promises to be an unforgettable, invaluable and one-of-a-kind career opportunity.

Applications of Machine Learning in Sickle Cell Disease

Kenneth Ataga, MD

Kenneth Ataga, MD

Scientific Chair

Kenneth Ataga received his medical degree from the School of Medicine at the University of Benin, Benin City, Nigeria in 1990. After completing a rotating internship at the University of Benin Teaching Hospital, he relocated to the United States when he underwent residency training in Internal Medicine at the State University of New York Health Science Center (currently Upstate Medical University) at Syracuse, NY. This was followed by a clinical and research fellowship in Hematology and Oncology at the University of North Carolina (UNC) at Chapel Hill under the mentorship of Dr. Eugene Orringer. He joined the faculty at UNC, Chapel Hill and rose to become Professor of Medicine and Director of the UNC Comprehensive Sickle Cell Program. In July, 2018, Dr. Ataga became the Plough Foundation Endowed Chair in Sickle Cell Disease and the inaugural Director of the Center for Sickle Cell Disease at the University of Tennessee Health Science Center (UTHSC) at Memphis.

Dr. Robert Davis, MD, MPH

Dr. Robert Davis, MD, MPH

Scientific Co-Chair

Robert Davis, MD, MPH is Director of the UTHSC Center for Biomedical Informatics. Dr. Davis has over 30 years’ experience conducting epidemiologic and health care research focused on perinatal epidemiology and pediatric/pregnancy drug and vaccine safety research. Dr. Davis is currently working on preeclampsia in Ghana, a CDC surveillance project for Sickle Cell Disease, and on predicting kidney disease in Sickle Cell Disease. Dr. Davis’ work focuses on artificial intelligence in medicine, including the application of AI for early identification of Parkinson’s disease, identification of sepsis in hospitalized patients, early recognition of kidney failure and of organ failure in sickle cell disease, and prediction of cardiomyopathy among childhood cancer survivors. Finally, Dr. Davis is the PI of the UTHSC 100K Genomes Project, a DNA biorepository, with over 28,000 consented participants and DNA on over 13,000—all linked to longitudinal electronic health record data.

Dr. Oguz Akbilgic, Ph.D.

Dr. Oguz Akbilgic, Ph.D.

10:10am - 10:40am Introduction to Machine Learning
Dr. Oguz Akbilgic is an Associate Professor of Cardiology and Biomedical Informatics at Wake Forest School of Medicine, Winston-Salem, NC. Prior to Wake Forest, he worked at Loyola University Chicago as an Associate Professor of Health Informatics and at the University of Tennessee Health Science Center as an Assistant Professor of Biomedical Informatics.
Dr. Akbilgic is a data scientist with formal training and expertise in artificial intelligence and statistical modeling. His research focus is the development of machine learning models for detection and risk prediction in domains including cardiovascular diseases, neurodegenerative diseases, sickle cell disease and surgery outcome. He also has interest in remote patient monitoring by applying artificial intelligence on data collected via wearables.
Dr. Robert Davis, MD, MPH

Dr. Robert Davis, MD, MPH

10:40am - 11:10am Utility of Machine Learning in Clinical Practice

Robert Davis, MD, MPH is Director of the UTHSC Center for Biomedical Informatics. Dr. Davis has over 30 years’ experience conducting epidemiologic and health care research focused on perinatal epidemiology and pediatric/pregnancy drug and vaccine safety research. Dr. Davis is currently working on preeclampsia in Ghana, a CDC surveillance project for Sickle Cell Disease, and on predicting kidney disease in Sickle Cell Disease. Dr. Davis’ work focuses on artificial intelligence in medicine, including the application of AI for early identification of Parkinson’s disease, identification of sepsis in hospitalized patients, early recognition of kidney failure and of organ failure in sickle cell disease, and prediction of cardiomyopathy among childhood cancer survivors. Finally, Dr. Davis is the PI of the UTHSC 100K Genomes Project, a DNA biorepository, with over 28,000 consented participants and DNA on over 13,000—all linked to longitudinal electronic health record data.

Dr. Amir Rahmani, Ph.D., MBA

Dr. Amir Rahmani, Ph.D., MBA

11:10am - 12:10pm Machine Learning to Predict Pain Crisis

Amir M. Rahmani is the founder of Health SciTech Group (healthscitech.org) at the University of California, Irvine (UCI) and the co-founder and Associate Director of the Institute for Future Health (futurehealth. uci.edu), a campus-wide Organized Research Unit (ORU), at UCI. He is an Associate Professor of Nursing, Computer Science, and EECS at UCI and is also a life-time adjunct professor (Docent) in embedded parallel and distributed computing at the Department of Computing of University of Turku (UTU), Turku, Finland. His research is in Internet-of-Things (IoT), machine learning, ubiquitous computing, bio-signal processing, health informatics, and big health data analytics. He is especially excited about novel sensing, computation/analytics, communication, and networking paradigms, applied to healthcare/medical and wellbeing applications. He has been leading several NSF, NIH, Academy of Finland, and European Commission funded projects on Smart Pain Assessment, Preventing Preterm-Birth, Family-centered Maternity Care, Stress Management in Adolescents, and Remote Elderly and Family Caregivers Monitoring. He has received numerous research excellence awards (e.g., 2x from Nokia Foundation) and best paper awards. He is the co-author of more than 300 peer-reviewed publications and the associate editor-in-chief of ACM Transactions on Computing for Healthcare.

Machine Learning and Hospital Readmission

Machine Learning and Hospital Readmission

12:40pm - 1:10pm Machine Learning and Hospital Readmission

Enrico M. Novelli, M.D., M.S., is an Associate Professor of Medicine at the University of Pittsburgh and an expert in sickle cell disease (SCD). He received his fellowship training in Hematology/Oncology at the University of Pittsburgh Medical Center (UPMC). Dr. Novelli has served as the Director of the UPMC Adult Sickle Cell Program since 2007 and as the Chief of the Section of Benign Hematology at UPMC since 2018. Dr. Novelli’s research focus is on vascular dysfunction and biomarker development in SCD, with a special interest in the area of cognitive dysfunction, for which he has received uninterrupted National Institutes of Health (NIH) funding. He has numerous publications in SCD and has served as a scientific reviewer for many journals, the NIH, and the American Heart Association. Dr. Novelli has been actively interested in advancing hematological care in low income settings and passionate about Global Hematology initiatives.

Dr. Akram Mohammed, MS, Ph.D.

Dr. Akram Mohammed, MS, Ph.D.

1:10pm - 1:40pm Machine Learning and Organ Failure

Dr. Akram Mohammed’s research interests broadly include health data science, machine learning, and multi-omics. Dr. Mohammed’s work at the University of Tennessee Health Science Center (UTHSC) focuses on designing and developing machine learning algorithms for predicting the onset of abnormal conditions using high-frequency physiological data streams from ICU bedside sensors. He also manages and analyzes whole-exome sequence and genotype data and develops and maintains the Biorepository and Integrative Genomics (BIG) Lab Information Management Systems (LIMS). Dr. Mohammed is keen on integrating multi-omics and electronic health record data to study complex diseases and host-pathogen interactions.

Dr. Fatma Gunturkun, Ph.D.

Dr. Fatma Gunturkun, Ph.D.

1:40pm - 2:10pm Machine Learning and Kidney Disease

DrFatma Gunturkun received the B.S. and M.S. degrees in statistics from Middle East Technical University, Turkey and Ph.D. degree in statistics from Dokuz Eylul University, Turkey. She is currently working as a Sr. Bio-Medical Analyst at University of Tennessee Health Science Center. Her research interests include the implementation of statistical modeling, machine learning, and data visualization for clinical decision making. She has been working on multiple artificial intelligence projects applied to medicine such as automated detection of hypertrophic cardiomyopathy, early recognition of rapid eGFR decline in sickle cell disease, identification of childhood cancer survivors at high risk of developing late-onset cardiomyopathy.

Dr. Vandana Sachdev, M.D.

Dr. Vandana Sachdev, M.D.

2:10pm - 2:40pm A Phenotypic Risk Score for Predicting Mortality in SCD

Dr. Sachdev is Director of the Echocardiography Laboratory and Senior Research Clinician in the Clinical Research Center at the National Institutes of Health in Bethesda, Maryland. She has been involved in cardiac imaging research for over twenty years. In collaboration with NIH sickle cell disease (SCD) investigators, her research has focused on the importance of tricuspid regurgitation velocity and diastolic dysfunction as risk markers in sickle cell disease (SCD). She is interested in the application of machine learning and artificial intelligence to risk stratification in SCD.

Dr. Colin Wu, Ph.D.

Dr. Colin Wu, Ph.D.

2:10pm - 2:40pm A Phenotypic Risk Score for Predicting Mortality in SCD

Colin O. Wu is senior Mathematical Statistician at the Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health (NHLBI/NIH). He is also adjunct professor of statistics at Georgetown University and the George Washington University. Dr. Wu’s research is focused on methods of machine learning and statistical modeling involving large and complex data and their applications in studies of heart, lung, blood and sleep disorders.

Dr. Swee Lay Thein, M.B., B.S., F.R.C.P., F.R.C.Path., D.Sc.

Dr. Swee Lay Thein, M.B., B.S., F.R.C.P., F.R.C.Path., D.Sc.

2:10pm - 2:40pm A Phenotypic Risk Score for Predicting Mortality in SCD

Dr. Thein is author or co-author of more than 300 peer-reviewed research publications, invited review articles, and book chapters. She has been honored for her research with awards from the U.K. Academy of Medical Sciences and the Academy of Life Sciences for Chinese in the U.K. Dr. Thein also was awarded a visiting professorship from Kuwait University and an honorary professorship in pathology from the University of Hong Kong. She serves on the editorial boards of the research journals Blood, Pathology, Annals of Haematology, Hemoglobin, and the American Journal of Hematology and is feature editor of the journal Blood’s Sickle Cell Disease hub, a micro-website that complements research published in the journal with links to articles, images and slideshows, and other multimedia.

Dr. Thomas J. Royston, Ph.D.

Dr. Thomas J. Royston, Ph.D.

2:40pm - 3:10pm Machine Learning and Acute Chest Syndrome

Thomas J. Royston, PhD, Professor and Head of Biomedical Engineering at the University of Illinois Chicago (UIC), earned his PhD in Mechanical Engineering from the Ohio State University in 1995. His NIH and NSF-supported research in acoustics applied to lung diagnostics and imaging soft tissue mechanics has been recognized with the NSF Career Award, the NIH NIBIB Nagy Award and the Acoustical Society of America Lindsay Award. He is a Fellow of the American Society of Mechanical Engineers and the American Institute for Medical and Biological Engineering, and has published 100 peer-reviewed journal articles.

Dr. Patrick Hines, MD, Ph.D.

Dr. Patrick Hines, MD, Ph.D.

3:10pm - 3:40pm Machine Learning and Biomarkers
Patrick Hines received his MD and PhD degrees from the University of North Carolina at Chapel Hill. He went on to complete a pediatrics residency and ICU fellowship at the Children’s Hospital of Philadelphia, and practiced pediatric cardiac critical care at the Children’s Hospital of Michigan.
Dr. Hines is an internationally recognized leader in the field of red blood cell health and founded Functional Fluidics to establish a gold standard for red blood cell health assessment, and currently serves as its CEO. Functional Fluidics is focused on sickle cell disease as a lead indication, their platform technology is the first blood-based biomarker shown to predict crises events in people with sickle cell, and has been used by big pharma and health care providers to monitor response to red blood cell modifying therapies.
Functional Fluidics has performed over 16,000 clinical adhesion and mechanical fragility tests for people living with sickle cell disease, and is working with healthcare providers to create a platform where biomarker data can be leveraged as a decision support tool to enable better preventative care and reduce the need for crisis intervention.

Become a Member of the Sickle Cell Research Society of America.

Price: $375

Whether you attend every year or this will be your first, THE FOUNDATION FOR SICKLE CELL DISEASE RESEARCH’S promises to be an unforgettable, invaluable and one-of-a-kind career opportunity.

Applications of Machine Learning in Sickle Cell Disease

September 22, 2022

Applications of Machine Learning in Sickle Cell Disease

$0.00$80.00

Please select your professional level. You can also pay by check – please make sure it is postmarked by September 21st, 2022. Mailing address is given at checkout.

Individuals with Sickle Cell Disease and their Caregivers attend for free – this option must be selected prior to checkout.