Computational Models for Type 1 Diabetes (T1D)

May 1, 2017 · 1 min read
projects

Develop novel computational disease models that could be used to identify factors that impact the rapid/slow progression of Type 1 Diabetes (“T1D”) in infants and young children who are genetically pre-disposed and may be pre-symptomatic clinically. Such methodologies and models may

  • incorporate heterogeneous features coming from multiple sites and assessments covering multiple aspects of T1D.
  • leverage diverse data sets to accommodate noise and uncertainty in study data.
  • provide comprehensive view of risk factors that impact the onset of T1D in different time horizons.
    Role I lead the efforts of developing AI models for disease progression modeling, which lead to publications in high-impact journals such as Lancet D&E (IF 45), Lancet C&A (IF 38), Diabetes Care (IF 19), etc.
    IBM Research. May 2017 - May 2022 .

    • IBM AI Research AIML
    Mohamed Ghalwash
    Authors
    Mohamed Ghalwash
    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.