Information Studies Student Alex Felmeister Wins 2017 iFellows Doctoral Award

Alex Felmeister, a PhD student in the Information Studies program at Drexel University's College of Computing & Informatics (CCI), recently received the 2017 iFellows Doctoral Fellowship award for his thesis tentatively titled "Advancing collaborative pediatric brain tumor research through temporally based predictive modeling of a large scale national clinical data research network" (see abstract below). 

Felmeister’s thesis centers around the examination of predictive models using temporally-based observational health data containing clinical data sets to annotate biological samples and data taken from cancer patients – specifically children suffering from highly lethal rare brain tumors.

He is the second CCI student to receive the iFellows award and is advised by Professor Xiaohua Tony Hu, PhD

He currently leads a group of software developers at Department of Biomedical and Health Informatics (DBHi) at the Children’s Hospital of Philadelphia (CHOP), specifically focused on the design, implementation and use of novel data driven applications that aid in translational research both operationally and scientifically. 

His general research interests include: translational biomedical informatics, scientific software integration, data driven application development and genomic, and clinical data integration needed in precision medicine in pediatric rare tumor research. 

In addition to his MS in Information Systems from Drexel University, he received a bachelor’s degree in communications and theater with a minor in finance from Temple University before returning to Drexel again to begin his doctoral studies in 2014.

The iFellows award is presented to select iSchool doctorate candidates and awards them two-year fellowships of $50,000 for academic research that supports the development of new technologies and other goals of the Coherence at Scale Program. This program is led by the Council on Library and Information Resources (CLIR) and Vanderbilt University.

Thesis Abstract:

Predictive models on temporal data in the form of observations in a sequence or series have helped make headway in utilizing large sets of clinical data collected from health systems in the electronic medical record and billing systems to predict outcomes, diagnoses and help make quicker decisions for health of populations with precision, treating the person and their disease based on genomic characteristics or predictive modeling. This concept of precision medicine is a heavy focus in research on diseases like cancer, and it is grounded in the individual’s cancer genomics which could have completely different molecular characteristics case-to-case and person-to-person. In fact, the tumor itself has different genomic characteristics when compared with other cells in the body. This proposal intends to look predictive models based on temporally based observational health data that is sequential in nature from clinical data sets to annotate the complex biology collected from cancer patients – specifically children suffering from highly lethal rare high-grade gliomas. This research focuses on the intersection two national longitudinal health data collection projects: The Children’s Brain Tumor Tissue Consortium (CBTTC) and the PEDSNet Clinical Data Research Network (CDRN) with the intention of harmonizing the two national projects as they grow to aid in the human annotation of biologically based resources with large scale automated health data networks. The research proposed herein contributes to the coherence broadly defined technical area of cross-platform workflow architectures to facility resource sharing and reuse in biomedical research. Specifically, the methods in this proposal use two common observational research methods utilized by two national organizations to come up with ways of doing large scale data and system integration for rare and deadly diseases by opening this data to more minds outside of purely the biomedical domain.

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