# Comparing Knowledge Source Integration Methods for Optimizing Healthcare Knowledge Fusion in Rescue Operation

arXiv: https://arxiv.org/abs/2510.09223 · HTML: https://mlsanity.com/paper/2510.09223/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 6 of 6)

- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Bayesian Networks in Healthcare: Distribution by Medical Condition](https://mlsanity.com/paper/2002.00224/) (2020)
- [Building Contextual Knowledge Graphs for Personalized Learning Recommendations using Text Mining and Semantic Graph Completion](https://mlsanity.com/paper/2401.13609/) (2024)
- [A Taxonomy of Explainable Bayesian Networks](https://mlsanity.com/paper/2101.11844/) (2021)
- [KIRETT: Knowledge-Graph-Based Smart Treatment Assistant for Intelligent Rescue Operations](https://mlsanity.com/paper/2508.07834/) (2025)
- [KIRETT -- A wearable device to support rescue operations using artificial intelligence to improve first aid](https://mlsanity.com/paper/2509.24934/) (2025)

## Abstract (arXiv)

In the field of medicine and healthcare, the utilization of medical expertise, based on medical knowledge combined with patients' health information is a life-critical challenge for patients and health professionals. The within-laying complexity and variety form the need for a united approach to gather, analyze, and utilize existing knowledge of medical treatments, and medical operations to provide the ability to present knowledge for the means of accurate patient-driven decision-making. One way to achieve this is the fusion of multiple knowledge sources in healthcare. It provides health professionals the opportunity to select from multiple contextual aligned knowledge sources which enables the support for critical decisions. This paper presents multiple conceptual models for knowledge fusion in the field of medicine, based on a knowledge graph structure. It will evaluate, how knowledge fusion can be enabled and presents how to integrate various knowledge sources into the knowledge graph for rescue operations.