CBIIT Speaker Series Page

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.

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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.

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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.

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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.

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