mlsanityData to 7 Oct 2026

Structural Positional Encoding for knowledge integration in transformer-based medical process monitoring

arXiv 2403.08836 ↗First posted 13 Mar 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

Predictive process monitoring is a process mining task aimed at forecasting information about a running process trace, such as the most correct next activity to be executed. In medical domains, predictive process monitoring can provide valuable decision support in atypical and nontrivial situations. Decision support and quality assessment in medicine cannot ignore domain knowledge, in order to be grounded on all the available information (which is not limited to data) and to be really acceptable by end users. In this paper, we propose a predictive process monitoring approach relying on the use of a em transformer, a deep learning architecture based on the attention mechanism. A major contribution of our work lies in the incorporation of ontological domain-specific knowledge, carried out through a graph positional encoding technique. The paper presents and discusses the encouraging experimental result we are collecting in the domain of stroke management.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 46 references that are in the map. First posted on arXiv 13 Mar 2024.

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18 of 46 references.

References · 18

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6 more
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