Enhancing Healthcare Decisions with Explainable Session-Based Recommendations

DOI

10.2991/978-2-38476-295-8_9How to use a DOI?

Keywords

Recommender Systems; Knowledge Graphs; Healthcare; Explainable AI; Session-Based Recommendation; Cold-Start Problem

Abstract

Recommender systems have demonstrated efficacy in various domains, but healthcare poses unique challenges with its complex medical knowledge and demand for explainable recommendations. This paper introduces a novel session-based, reasoning-focused recommender system for healthcare professionals during patient consultations. The system utilizes a hybrid knowledge graph integrating medical ontologies, real-time patient data, and external resources. Advanced reasoning over this graph infers diagnoses, predicts drug interactions, and suggests treatments, particularly effective in cold-start scenarios. It dynamically updates suggestions during consultations as new data emerges, prioritizing transparency by providing clear explanations for recommendations. Evaluation on medical datasets and user studies shows improved diagnostic accuracy, faster diagnosis times, and high satisfaction with explainable recommendations. This research advances knowledge-based reasoning in healthcare, enhancing decision-making and patient care.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jiahao Zhu
PY  - 2024
DA  - 2024/10/21
TI  - Enhancing Healthcare Decisions with Explainable Session-Based Recommendations
BT  - Proceedings of the 4th International Conference on Public Administration, Health and Humanity Development (PAHHD 2024)
PB  - Atlantis Press
SP  - 69
EP  - 75
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-295-8_9
DO  - 10.2991/978-2-38476-295-8_9
ID  - Zhu2024
ER  -