About
Sulaiman Khan completed his PhD in Computer Science with DAQIQ and now works with the program as a Research Assistant.
His doctoral research fused imaging, ECG, genetic, laboratory and lifestyle data from Qatar Biobank to model how diabetes risk changes over time, and to study complication pathways such as renal and respiratory health. His DXA study was the first to use longitudinal bone scans to predict future diabetes in Qatar.
Research focus: Longitudinal multimodal AI for early prediction of type 2 diabetes and its complications
Thesis
Longitudinal Analysis of Risk Factors Associated with Diabetes Using Multimodal Data and Machine Learning
Talks
Publications with DAQIQ 5
-
Artificial intelligence–based diabetes risk prediction from longitudinal DXA bone measurements
Khan S, Shah Z
Scientific Reports 15: 25706, 2025
Journal articleLongitudinal analysis of risk factors associated with diabetes using multimodal data and machine learning
-
Khan S, Biswas MR, Shah Z
Artificial Intelligence in Healthcare (AIiH 2025), Lecture Notes in Computer Science 16038, Springer, 2025
Conference paperTalk and slidesLongitudinal analysis of risk factors associated with diabetes using multimodal data and machine learning
-
Early prediction of type 2 diabetes using multimodal data and tabular transformers
Khan S, Biswas MR, Shah Z
3rd International Conference on Foundation and Large Language Models (FLLM 2025): 592–599, IEEE, 2025
Conference paperFree versionTalk and slidesLongitudinal analysis of risk factors associated with diabetes using multimodal data and machine learning
-
Khan S, Shah Z
IEEE International Conference on Information Reuse and Integration for Data Science (IRI 2025): 289–294, 2025
Conference paperTalk and slidesLongitudinal analysis of risk factors associated with diabetes using multimodal data and machine learning
-
Genetic biomarkers and machine learning techniques for predicting diabetes: systematic review
Khan S, Mohsen F, Shah Z
Artificial Intelligence Review 58(2): 41, 2024
Journal articleLongitudinal analysis of risk factors associated with diabetes using multimodal data and machine learning