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New methods for explaining survival models’ predictions

Hello there, Abdallah Alabdallah! You recently finished your PhD in Information Technology at Halmstad University, with a thesis titled “Towards trustworthy survival analysis with machine learning models”. What is the thesis about?

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“My thesis focuses on survival analysis using machine learning models, particularly addressing challenges in explainability and performance. It introduces new methods for explaining survival models’ predictions and optimising their performance while maintaining explainability, crucial for applications like medical studies and predictive maintenance. I collaborated with researchers from Halmstad University and other institutions, including AGH University of Science and Technology and Jagiellonian University in Kraków, both in Poland.”

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Abdallah Alabdallah defended his PhD thesis in January 2025.

Some of Abdallah Alabdallah’s findings

  • Effective post-hoc methods to explain survival models, including feature attribution and counterfactual explanations
  • Enhanced performance of survival models through novel loss functions and algorithms to handle noisy data
  • An inherently explainable survival model (CoxSE) that balances transparency and predictive power.
Electrical bus. Photo.

Abdallah Alabdallah’s research can improve decision-making in areas such as industrial safety.

Abdallah Alabdallah’s educational background

Abdallah Alabdallah holds a Bachelor’s degree in Software Engineering from Damascus University, Syria, and a Master’s degree in Intelligent Systems from Halmstad University. His interest in the subject of explainability began while working on his Master’s thesis, which focused on explaining AI models designed to process and analyse images. He chose Halmstad University for his PhD studies because of its research in machine learning and its interdisciplinary approach to AI applications, as well as the University’s collaboration with important industry partners.

Abdallah Alabdallah’s PhD studies have improved how AI predicts and explains the expected length of time until an event occurs, like how long a machine will work or how a patient might respond to treatment. He has created tools to make these predictions more accurate and easier to understand, helping people trust AI in critical areas like healthcare and industrial safety.

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