Predictive Modeling of Patient State and Therapy Optimization
Aug 1, 2011
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1 min read
This project develops and validates effective predictive modeling technology to achieve the following sepsis treatment related aims on high dimensional and noisy data at a clinically relevant scale
AIM 1 - Personalized sepsis therapy optimization for an individual patient’s state improvement.
AIM 2 - Early diagnosis of sepsis and accurate detection of change in the state of sepsis., and
AIM 3 - Gene expression analysis for sepsis biomarkers identification.
Role I was leading the first and second aims where I developed novel ideas for early detection of sepsis, which resulted in publication in high prestigious conferences such as KDD and SDM.
Temple University. DARPA (DARPA-N66001-11-1-4183). Aug 2011 - Aug 2015 .
Authors
Associate Professor
Mohamed Ghalwash is an Associate Professor of Artificial Intelligence at Ain Shams University / Zewail City of Science, Technology, and Innovation. Ghalwash is interested in developing machine learning and AI models for discovery of interpretable spatio-temporal patterns from complex events and exploiting their utilities in practical high impact real-world applications, focusing on analytics for healthcare, health behavior, decision support, and to support the generation of real world evidence from medical data, wearable devices and instrumented environments. He also has more than ten years of expertise in the software engineering industry, developing software that are currently being deployed, where he engaged in all phases of the project life cycle for the development of large business applications. My long-term goal is to put my footprints in the field of pattern recognition through contribution of original ideas and translating those ideas to end applications and products.