Explainable decision support through the learning and visualization of preferences from a formal ontology of antibiotic treatments
Open accessAbstract
The aim of eXplainable Artificial Intelligence (XAI) is to design intelligent systems that can explain their predictions or recommendations to humans. Such systems are particularly desirable for therapeutic decision support, because physicians need to understand rcommendations to have confidence in their application and to adapt them if required, e.g. in case of patient contraindication. We propose here an explainable and visual approach for decision support in antibiotic treatment, based on an ontology. There were three steps to our method. We first generated a tabular dataset from the ontology, containing features defined on various domains and n-ary features. A preference model was then learned from patient profiles, antibiotic features and expert recommendations found in clinical practice guidelines. This model made the implicit rationale of the expert explicit, including the way in which missing data was treated. We then visualized the preference model and its application to all antibiotics available on the market for a given clinical situation, using rainbow boxes, a recently developed technique for set visualization. The resulting preference model had an error rate of 3.5% on the learning data, and 5.2% on test data (10-fold validation). These findings suggest that our system can help physicians to prescribe antibiotics correctly, even for clinical situations not present in the guidelines (e.g. due to allergies or contraindications for the recommended treatment).
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Lamy et al. designed an additional system that incorporated post-hoc explainability and provided local explanations of the preference model using rainbow boxes.
“An additional system that incorporated post-hoc explainability was designed by Lamy et al. [123] and provided local explanations of the preference model using rainbow boxes [56].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [123]
Most studies used visualization as a key aspect to enhance explainability, either with SHAP plots, regional abnormality maps, rainbow boxes, or the attention mechanism that highlighted important words leading to a prediction.
“Most studies used visualization as a key aspect to enhance explainability, either with SHAP plots [118,120], regional abnormality maps [119], rainbow boxes [123,126], or the attention mechanism that highlighted the important words that lead to a prediction [122].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [123]
The remaining CDSS implemented model-agnostic post-hoc methods for local explanations.
“The remaining CDSS implemented model-agnostic post-hoc methods for local explanations [119,123].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [123]
Lamy et al. [123] visualised the created preference model using rainbow boxes; the method was agnostic, post-hoc, and local.
“Lamy et al. [123] Visualised the created preference model using rainbow boxes [131] agnostic post-hoc local”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [123]
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Antoniadi, A. M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B. A., & Mooney, C. (2021) · Applied Sciences · doi:10.3390/app11115088 · Cited by 629 · see citing works
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“…foundational studies explored a wide range of interpretability techniques, including visualizing ontology-based preferences for decision support [42], using visual case-based reasoning in oncology [38], developing inherently interpretable models like RETAIN for electronic health records [43],…”
- Authors
- Lamy, J.-B., Sedki, K., & Tsopra, R.
- Affiliations
- Inserm (FR)
- Corresponding author
- Lamy, Jean-Baptiste
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 104
- Pages
- 103407
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W3011778563
- Cited by
- 25
- References
- 65
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 5 · 2023: 4 · 2022: 4 · 2021: 5 · 2020: 3
- FWCI
- 1.57
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- HAL (submittedVersion)
- Funders
- Agence Nationale de Sécurité du Médicament et des Produits de Santé (AAP 2016)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- explainable AI, antibiotic therapy, therapeutic decision support, formal ontology, preference learning, set visualization, antibiotic prescribing, patient characteristics, missing data handling
- MeSH terms
- Anti-Bacterial Agents, Artificial Intelligence, Humans, Learning, Decision Support Systems, Clinical
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