Informatics Seminar Series

Clinical Outcome Prediction through Deep Learning

Clinical Outcome Prediction through Deep Learning

Accurately predicting clinical outcomes in advance can benefit both healthcare providers and patients, though it remains a challenging task. Artificial Intelligence (AI) has recently generated much excitement due to the breakthroughs made by Deep Neural Networks (DNNs) on many tasks such as image classification and speech recognition. Inspired by those developments, there has been great interest in applying DNNs to the biomedical domain. In this seminar, I will present a DNN-based predictive modeling approach applied to two clinical use cases. DNN models are usually considered as black boxes which would hinder their acceptance by clinicians. Therefore, we also developed a novel method for explaining the predictions of our DNN models.

Link to recording: 

Streaming: https://gwu.webex.com/gwu/ldr.php?RCID=6b4f78fcca2d6e47c6f6576c3e8c613e

Download: https://gwu.webex.com/gwu/lsr.php?RCID=535725886993615bce6077a2e09a7a1f


The FABRIC environment

The FABRIC environment: Architectural Features and Big Data Analytics.

The Flexible Architecture for Building Research Informatics Collaborations (FABRIC) is an informatics platform (in development) which offers a service oriented research toolbox that investigators, clinicians, and patient advocates can use to easily access a wide array of data repositories integrated with customizable query tools. This cloud environment is able to support the rapid formation of dedicated cross-domain research teams, the sharing of raw and de-identified datasets in secured enclaves, and the access to a suite of advanced analytics tools commonly used throughout clinical translational research (CTR). This seminar will focus on some of the architectural features of FABRIC, its connection to the Colonial One HPC cluster, and the integration of the HealthFacts database.

Link to recording: 

Streaming: https://gwu.webex.com/gwu/ldr.php?RCID=660e0fb4a361e2dbe81583aeceb2d7a7

Download: https://gwu.webex.com/gwu/lsr.php?RCID=c5289e6cdc48877f01df247dd04b5704