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Upcoming Speaker: 

April 25, 2018

Dr. Vahan Simonyan 

Wellness and Health Information Secure Exchange (WHISE): A Scalable Economy for Healthcare Data Markets

An invitation: If you are interested in presenting your work to our diverse audience of informaticists; basic, translational, and clinical researchers; software developers; and others interested in exploring the uses of informatics in cancer research, contact Eve Shalley at eve.shalley@nih.gov or 240-276-5194.

 

Welcome to the CBIIT Speaker Series Wiki 

The NCI Center for Biomedical Informatics and Information Technology (CBIIT) Speaker Series presents talks from innovators in the research and informatics community. The biweekly presentations allow thought leaders to share their work and discuss trends across a diverse set of domains and interests. The goals of the Speaker Series are: to share leading edge research; to inform the community of new tools, trends, and ideas; to inspire innovation; and to provide a forum from which new collaborations can begin.

Speakers represent many different institutions, and the topics they address are wide-ranging. View a list of all past speakers, and view their presentations on our NCI CBIIT Speaker Series YouTube playlist!

For help accessing NCI CBIIT Speaker Series files, go to Help Downloading Files.

Location: 9609 Medical Center Drive, Rockville, Maryland 20850

Speaker Series Guidelines for Speakers: Download Word document

Questions or suggestions? If you have questions or would like to recommend a speaker, please email Eve Shalley at eve.shalley@nih.gov

Please refer to the Speaker Calendar below for upcoming speakers.

 

Upcoming Speakers:

April 25, 2018: Dr. Vahan Simonyan, School of Medicine and Health Sciences, George Washington University (presenting onsite at NCI)

May 9, 2018: Dr. Andrey Fedorov, Brigham and Women's Hospital and Harvard Medical School (presenting via WebEx)

June 6, 2018: Dr. Allen Dearry, NCI (presenting onsite)

June 20, 2018: Dr. Casey Greene, University of Pennsylvania, School of Medicine (presenting via WebEx)

July 18, 2018: Dr. Daoud Meerzaman, NCI (presenting onsite)

October 10, 2018: Helga Thorvaldsdottir, Broad Institute; Jim Robinson, UC San Diego; Mary Goldman, UC Santa Cruz; and Alex Krasnitz, Ph.D., Cold Spring Harbor National Lab (presenting via WebEx)

CBIIT Speakers

 

During this presentation, Dr. Simonyan will discuss WHISE for creating incentives and promoting the liberation of health data through patient ownership, exchange of proprietary data, and by adding value through intellectual and analytic insights. The WHISE technology provides a service based architecture where the exchange between consumer and owner of information can happen with data or with derived and computed information. It allows assetization of data and commoditization of data access.

Session details...

Predicting treatment response and the course of a patient’s disease is critical in selecting therapy and in helping patients to plan their lives. Despite the rich data produced by genomic and imaging platforms, the accuracy of prognostication for patients diagnosed with cancer can be highly variable, often relying on classification by only a handful of molecular biomarkers or subjective interpretation of histology. While deep learning has emerged as a powerful technology for learning from unstructured images or other high-dimensional data, its application has largely focused on classification and has not widely explored predicting the timing of disease progression, overall survival, or other time-to-event clinical outcomes. In this talk, Dr. Cooper will discuss recent advances in developing deep-learning based survival models for predicting cancer outcomes from genomic and digital pathology imaging data. He will show how conventional survival models can be combined with convolutional networks or other neural networks to learn patterns associated with patient outcomes in digital pathology images or genomic signatures. Using gliomas as a driving use case, he will describe how these models can combine histology and genomics to provide unified and highly accurate predictions of overall survival, and illustrate how these models can be deconstructed to improve validation and reveal biological insights.

Session details...

Oncology is a fertile field for the development of what the Institute of Medicine terms a “Rapid Learning Health System.” To this end, the American Society of Clinical Oncology (ASCO) created CancerLinQ, a system designed to enable a virtuous cycle in which research drives care and data from routine care helps to inform the next generation of treatment standards and research questions. CancerLinQ is a community of oncology practices that have joined together in this goal, as well as a specific technology platform that enables the collection, aggregation and harmonization of data from Electronic Health Record (EHR) systems for the purpose of improving quality of care for patients. Methods used to extract, process and manage data in CancerLinQ, as well as general properties of the CancerLinQ data sets will be discussed.

Session details...

The Observational Health Data Sciences and Informatics (OHDSI) network has mapped data from more than 50 databases (that contain information on more than 400 million patients) to the OMOP common data model and it uses an open science approach to conduct distributed research. It has analyzed data from 11 databases (containing information on more than 250 million patient records) to ascertain the sequence of treatments over three years in patients with depression, diabetes or hypertension.  This presentation will describe how electronic health records and claims data can be used to ascertain treatments received by cancer patients; present data on sequence of treatments for cancer patients with depression, diabetes or hypertension, and the accuracy of cancer care documentation; and discuss approaches to improve abstraction of cancer information from electronic health records.

Session details...

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