6 results on '"Suzi Birz"'
Search Results
2. Sociome Data Commons: A scalable and sustainable platform for investigating the full social context and determinants of health
- Author
-
Sandra Tilmon, Sharmilee Nyenhuis, Anthony Solomonides, Bruno Barbarioli, Ankur Bhargava, Suzi Birz, Kathryn Bouzein, Celine Cardenas, Bradley Carlson, Ellen Cohen, Emily Dillon, Brian Furner, Zhong Huang, Julie Johnson, Nivedha Krishnan, Kevin Lazenby, Kaitlyn Li, Sonya Makhni, Doriane Miller, Jonathan Ozik, Carlos Santos, Marc Sleiman, Julian Solway, Sanjay Krishnan, and Samuel Volchenboum
- Subjects
Asthma ,health disparities ,Chicago ,data commons ,SDOH ,Medicine - Abstract
Abstract Background/Objective: Non-clinical aspects of life, such as social, environmental, behavioral, psychological, and economic factors, what we call the sociome, play significant roles in shaping patient health and health outcomes. This paper introduces the Sociome Data Commons (SDC), a new research platform that enables large-scale data analysis for investigating such factors. Methods: This platform focuses on “hyper-local” data, i.e., at the neighborhood or point level, a geospatial scale of data not adequately considered in existing tools and projects. We enumerate key insights gained regarding data quality standards, data governance, and organizational structure for long-term project sustainability. A pilot use case investigating sociome factors associated with asthma exacerbations in children residing on the South Side of Chicago used machine learning and six SDC datasets. Results: The pilot use case reveals one dominant spatial cluster for asthma exacerbations and important roles of housing conditions and cost, proximity to Superfund pollution sites, urban flooding, violent crime, lack of insurance, and a poverty index. Conclusion: The SDC has been purposefully designed to support and encourage extension of the platform into new data sets as well as the continued development, refinement, and adoption of standards for dataset quality, dataset inclusion, metadata annotation, and data access/governance. The asthma pilot has served as the first driver use case and demonstrates promise for future investigation into the sociome and clinical outcomes. Additional projects will be selected, in part for their ability to exercise and grow the capacity of the SDC to meet its ambitious goals.
- Published
- 2023
- Full Text
- View/download PDF
3. P086: Advancing Pediatric Hodgkin Lymphoma Research Through NODAL
- Author
-
Jamie E. Flerlage, Suzi Birz, Sharon M. Castellino, Burton Appel, Brian Furner, Luca Graglia, Tara O. Henderson, David Hodgson, Bradford S. Hoppe, Justine Kahn, Frank G. Keller, Sandy Kessel, Chen Li, Mei Li, John Lucas, Kathleen Mccarten, Monika Metzger, Sarah Milgrom, Susan K. Parsons, Qinglin Pei, Yue Wu, Yiwang Zhou, Michael Watkins, Sam Volchenbaum, and Kara M. Kelly
- Subjects
Diseases of the blood and blood-forming organs ,RC633-647.5 - Published
- 2022
- Full Text
- View/download PDF
4. Creating a data commons: The INternational Soft Tissue SaRcoma ConsorTium (INSTRuCT)
- Author
-
Kirk D, Wyatt, Suzi, Birz, Douglas S, Hawkins, Veronique, Minard-Colin, David A, Rodeberg, Monika, Sparber-Sauer, Gianni, Bisogno, Ewa, Koscielniak, Gian Luca, De Salvo, Martin, Ebinger, Johannes H M, Merks, Suzanne L, Wolden, Wei, Xue, and Samuel L, Volchenboum
- Subjects
Oncology ,Pediatrics, Perinatology and Child Health ,Rhabdomyosarcoma ,Humans ,Sarcoma ,Soft Tissue Neoplasms ,Hematology ,Child - Abstract
In this article, we will discuss the genesis, evolution, and progress of the INternational Soft Tissue SaRcoma ConsorTium (INSTRuCT), which aims to foster international research and collaboration focused on pediatric soft tissue sarcoma. We will begin by highlighting the current state of clinical research for pediatric soft tissue sarcomas, including rhabdomyosarcoma and non-rhabdomyosarcoma soft tissue sarcoma. We will then explore challenges and research priorities, describe the development of INSTRuCT, and discuss how the consortium aims to address key research priorities.
- Published
- 2022
5. Pediatric Cancer Data Commons: Federating and Democratizing Data for Childhood Cancer Research
- Author
-
Gianni Bisogno, Nathalie Gaspar, Mareike Rasche, Alejandro Plana, Suzi Birz, Luca Graglia, E. Anders Kolb, Maura A. Kush, Ewa Koscielniak, Susan L. Cohn, Douglas S. Hawkins, Dirk Reinhardt, Katherine A. Janeway, Nicole Dussault, James Nicholson, Brian Furner, Stefanie Hecker-Nolting, C. Michel Zwaan, Samuel L. Volchenboum, Monica Palese, A. Lindsay Frazier, and Andrew D.J. Pearson
- Subjects
Knowledge management ,Data collection ,Biomedical Research ,business.industry ,Corporate governance ,MEDLINE ,Medizin ,General Medicine ,Plan (drawing) ,Genomics ,Medical Oncology ,Pediatric cancer ,United States ,Blueprint ,Political science ,Neoplasms ,Sustainability ,Humans ,Child ,Ecosystem ,Commons ,business - Abstract
The international pediatric oncology community has a long history of research collaboration. In the United States, the 2019 launch of the Children's Cancer Data Initiative puts the focus on developing a rich and robust data ecosystem for pediatric oncology. In this spirit, we present here our experience in constructing the Pediatric Cancer Data Commons (PCDC) to highlight the significance of this effort in fighting pediatric cancer and improving outcomes and to provide essential information to those creating resources in other disease areas. The University of Chicago's PCDC team has worked with the international research community since 2015 to build data commons for children's cancers. We identified six critical features of successful data commons design and implementation: (1) establish the need for a data commons, (2) develop and deploy the technical infrastructure, (3) establish and implement governance, (4) make the data commons platform easy and intuitive for researchers, (5) socialize the data commons and create working knowledge and expertise in the research community, and (6) plan for longevity and sustainability. Data commons are critical to conducting research on large patient cohorts that will ultimately lead to improved outcomes for children with cancer. There is value in connecting high-quality clinical and phenotype data to external sources of data such as genomic, proteomics, and imaging data. Next steps for the PCDC include creating an informed and invested data-sharing culture, developing sustainable methods of data collection and sharing, standardizing genetic biomarker reporting, incorporating radiologic and molecular analysis data, and building models for electronic patient consent. The methods and processes described here can be extended to any clinical area and provide a blueprint for others wishing to develop similar resources.
- Published
- 2021
6. Prediction of Neutropenic Events in Chemotherapy Patients: A Machine Learning Approach
- Author
-
Holly Wiberg, Suzi Birz, Dimitris Bertsimas, Peter Yu, Pat Montanaro, Michelle Schneider, and Jeff Mather
- Subjects
Chemotherapy ,Neutropenia ,business.industry ,medicine.medical_treatment ,MEDLINE ,Cancer ,General Medicine ,medicine.disease ,Machine learning ,computer.software_genre ,Machine Learning ,Risk Factors ,Medicine ,Electronic Health Records ,Humans ,Artificial intelligence ,business ,computer ,Febrile neutropenia ,Algorithms - Abstract
PURPOSE Severe and febrile neutropenia present serious hazards to patients with cancer undergoing chemotherapy. We seek to develop a machine learning–based neutropenia prediction model that can be used to assess risk at the initiation of a chemotherapy cycle. MATERIALS AND METHODS We leverage rich electronic medical records (EMRs) data from a large health care system and apply machine learning methods to predict severe and febrile neutropenic events. We outline the data curation process and challenges posed by EMRs data. We explore a range of algorithms with an emphasis on model interpretability and ease of use in a clinical setting. RESULTS Our final proposed model demonstrates an out-of-sample area under the receiver operating characteristic curve of 0.865 (95% CI, 0.830 to 0.891) in the prediction of neutropenic events on the basis of only 20 clinical features. The model validates known risk factors and offers insight into potential novel clinical indicators and treatment characteristics that elevate risk. It relies on factors that are directly extractable from EMRs, provided a tool can be easily integrated into existing workflows. A cost-based analysis provides insight into optimal risk thresholds and offers a framework for tailoring algorithms to individual hospital needs. CONCLUSION A better understanding of neutropenic risk on an individual level enables a more informed approach to patient monitoring and treatment decisions.
- Published
- 2021
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.