Patient Representation
Aug 1, 2020
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1 min read
Develop and test patient and disease representations for clinical models. Jointly learning embedding for different domains and further fine tuning or enhancing embeddings for some domains when limited amounts of data is available present significant AI challenges that will force us to produce methodological innovations. We build a comprehensive library for representation learning methods and develop novel methods to address these presented issues. These representation algorithms will be assessed based on their sensitivity to data characteristics including fairness.
Role I technically lead the task force for patient representation for developing novel fair AI models.
IBM Research. Oct 2020 - Present .
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.