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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2403.08836 |
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| _version_ | 1866912908266438656 |
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| author | Irwin, Christopher Dossena, Marco Leonardi, Giorgio Montani, Stefania |
| author_facet | Irwin, Christopher Dossena, Marco Leonardi, Giorgio Montani, Stefania |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08836 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Structural Positional Encoding for knowledge integration in transformer-based medical process monitoring Irwin, Christopher Dossena, Marco Leonardi, Giorgio Montani, Stefania Machine Learning Artificial Intelligence 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. |
| title | Structural Positional Encoding for knowledge integration in transformer-based medical process monitoring |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2403.08836 |