Florence Nightingale (1820-1910), an English statistician and the founder of modern nursing, was born in Florence, Italy. She was an inspirational person with a passion for serving and care. As an example, she cared for wounded soldiers in the Crimean War. Nightingale was not only the founder of nursing, but she was also an acclaimed and compassionate statistician who, among others, pioneered visual statistics.
To honor this pioneer, the Florence Nightingale Award will be given to the most outstanding eligible candidate who has demonstrated exceptional scholarship. Candidates with a record of service and commitment to honorable causes, who have helped to raise the standing of disadvantaged groups in society including women and minorities, have been identified as Finalists and will participate in this virtual session, open to IBS members and International Biometric Society attendees free of charge. We thank the Caucus for Women in Statistics and Data Science for co-sponsoring this session and the awards program.
Participating in this session are the following Finalists:
Serifat Folorunso – Autistic Spectrum Disorder Screening Classification with Machine Learning Approaches
Brief Summary: The degree of severity and range of symptoms of Autistic Spectrum Disorder (ASD) differ widely from young children, and their medical conditions are determined by their age, psychological feature, and speech ability, and accompanied disorders. Early detection of this disease can significantly reduce medical costs. However, the waiting periods for a diagnosis of autism spectrum disorder (ASD) are lengthy, and treatments are exorbitantly costly. In this era of Big-data, conventional techniques for disease classification and prediction have proven unsuccessful when compared to machine learning approaches.
Ashefet Agete Mengste - Bayesian Survival Analysis of Factors of Mortality of Cardiovascular Diseases Patients
Brief Summary: Cardiovascular diseases (CVDs) represent a significant global health burden, with a substantial portion of related mortality concentrated in low- and middle-income countries. Understanding the prognostic factors that influence the survival of cardiovascular disease patients is of paramount importance for effective clinical management and public health interventions. The objective of this study was to determine the mortality risk factors among cardiovascular disease patients.
Shongkour Roy - Disparities in the temporal variations of survival rates of children with chronic and acute myeloid leukemia in the US
Brief summary: About 120 new cases of chronic myeloid leukemia (CML) and 500 cases of acute myeloid leukemia (AML) occur each year in the US. Treatment advances have significantly improved the survival rates among these patients. However, disparities may exist, and this study aims to assess the temporal variations in relative survival among children (aged 0-18 years) with CML and AML based on age, gender, and race in the US from 1997 to 2020.
Seyifemickael Tilema - A Hierarchical Bayesian Approach to Small Area Estimation of Health Insurance Coverage in Ethiopian Administrative Zones
Brief summary: Survey data are extensively used to provide reliable direct estimates for national and regional levels with enough sample sizes. However, zones are unplanned domains and have seldom large enough sample sizes to produce direct estimates of adequate precision. Small area estimation (SAE) overcome the challenges of producing unreliable estimates of parameters of interest for which the sample sizes are too small, even zero.
Congratulations to our Finalists! Please join us to honor them and enjoy their 15-minute talks. This session will be held via Zoom.
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Peter Doherty, CAE
Executive Director
International Biometric Society
Washington, DC USA
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