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Disease Progression Forecasting Using the Informer Transformer Architecture
Mirza, Umair Aamir (2025) Disease Progression Forecasting Using the Informer Transformer Architecture.
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| Full Text Status: | Access to this publication is restricted |
| Embargo date: | 7 July 2026 |
| Abstract: | Accurate prediction of disease progression is essential to enable timely clinical interventions and support personalized treatment strategies in both chronic and acute care settings. While recurrent neural networks (RNNs) have traditionally dominated time series modeling in healthcare, transformer-based architectures have recently shown considerable promise. This study evaluates the applicability of the Informer architecture, a state-of-the-art transformer model for long-sequence forecasting, on longitudinal clinical data from the Parkinson’s Progression Markers Initiative (PPMI) dataset. Multiple model variations are explored, including different temporal embedding techniques, feature selection strategies, and multi-task learning configurations. To enhance clinical interpretability, explainability methods such as self-attention visualizations, permutation importance, and GradientSHAP are employed. The results show that the Informer architecture can be viable for forecasting tasks within clinical health domains. However, challenges remain with respect to handling feature saturation and effectively capturing temporal dependencies in short-sequence clinical data. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Business & IT BSc (56066) |
| Link to this item: | https://purl.utwente.nl/essays/107560 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
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