IEEE R2 WIE Meeting and Talk:"Biomedical Unstructured Data Integration and Challenges."

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WIE Speaker Series: Sharing a WIE Member’s Career Journey from Personal, Professional, and Technical Views


The IEEE R2 Women in Engineering (WIE) Affinity Goups meeting is on June 13, 2022. All (members and guests) are welcome! We would love for all of our 11 WIE Chairs stay engaged and share "what's new?"

"While girls may be enthusiastic about a career in computer science, not many of them follow that dream. There are many reasons for that, one of them being the lack of inspiring female role models" [Source: computerscience.org] 

One of those models is Dr. Priya Deshpande, who recently received her PhD in Computer Science from DePaul University, Chicago. Priya will share her career journey as a women in tech. Her talk, "Biomedical Unstructured Data Integration and Challenges" will discuss the importance of data-driven research in advancing biomedical knowledge and applications, such as more efficient healthcare delivery systems, improved clinical diagnostic processes, novel biomedical discoveries, and more. However, despite the huge amounts of clinical and biomedical data generated in hospitals, clinics and research institutes, datasets are often not shared. There is lack of data integration across information systems. A solution to integrating heterogenous unstructured biomedical data will be introduced. 




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  • Date: 13 Jun 2022
  • Time: 07:00 PM to 08:00 PM
  • All times are (UTC-05:00) Eastern Time (US & Canada)
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  • Women in Engineering 

    Contact: c.carey@ieee.org

  • Starts 24 May 2022 12:00 PM
  • Ends 13 June 2022 04:00 PM
  • All times are (UTC-05:00) Eastern Time (US & Canada)
  • No Admission Charge


  Speakers

Dr. Priya Deshpande Dr. Priya Deshpande of Christopher Newport University

Topic:

Biomedical Unstructured Data Integration and Challenges

Vast amounts of clinical and biomedical research data are generated in hospitals, clinics, laboratories, and research institutes. These are considered a primary force in enabling data-driven research toward advancing biomedical knowledge and present the potential for introducing new efficiencies in healthcare delivery. Data-driven research can have many goals, including but not limited to improved diagnostics processes, novel biomedical discoveries, epidemiology, and education. Finding and gaining access to relevant data and metadata across multiple datasets that is necessary to achieve these goals, however, remains elusive. Data re-usability is a highly desirable goal, both for advancing science and for replicating or validating results of previous studies. Recognizing this need, publishers and funding bodies often require researchers to submit data generated as a result of their work and make it available to the research community to ensure reproducibility. The National Institutes of Health (NIH) encourages researchers to provide access to their research data through the NIH Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) Initiative. However, in the healthcare domain, datasets are often not shared because of security concerns, geographic disconnect, different data governance policies, heterogeneous nature of the data, lack of integration across information systems, or limitations of retrieval models. From our survey of the research literature, we learned that current hospital systems and public search engines do not support integration of public and internal data, and have limited natural language search capabilities. Moreover, data preparation (i.e., finding relevant data sources, extracting data, data cleaning, and data integration) accounts for 80% of a data scientist work. Significant time and effort can be saved by developing techniques that provide integration and text-based search capability that addresses the current limitations.

This research extensively investigates the problem of biomedical data integration, with a solution for integrating heterogeneous unstructured biomedical data by considering semantical similarity of data elements, and domain-aware algorithms for text-based query search. This research incorporates: a) knowledge extraction techniques for content and coverage analysis of medical data sources that can guide the integration of these data, b) workflow for integration of heterogeneous medical data sources, c) design of a unified database schema that incorporates extracted semantic annotation, and d) faster query lookup across the integrated data repository. Although we focus on the medical field, this work could also be transferred to other domains with heterogeneous data sources and domain-specific ontologies.

Biography:

Priya Deshpande finished her PhD in Computer Science from DePaul University, Chicago and is currently working as an Assistant Professor in Christopher Newport University, Virginia. Prior coming to DePaul University, Priya was working as an Assistant Professor at the University of Pune, India (2005-2016). Her research interest is in data sciences, big data analytics, databases, and computer-aided diagnosis. Her research extensively investigates the problem of biomedical data integration with heterogeneous and unstructured data elements.

Email:

Address: One Avenue of the Arts , , Newport News, Virginia, United States, 23606





Agenda

Draft Agenda

7:00pm  Call to order, Welcome and Roll Call (R2 WIE AG Chairs)

7:15pm  Introduce Speaker / Presentation

7:35pm  Brief Reports / Updates (All R2 WIE AG Chairs)

7:55pm  Any new business 

8:00pm  Close 



We welcome any IEEE WIE Affinity Group or other IEEE Section Technical Chapter/Affinity Group to join us co-host the event. Please send email to Carole Carey <c.carey@ieee.org>  and express your interest.