# A Strategy-Driven Semantic Framework for Precision Decision Support in Targeted Medical Fields
## 1 Introduction  
Significant challenges arising from healthcare modernization and digital transformation, such as adopting home-based care, precision treatments, and personalized or remote drug delivery systems, are being addressed through Healthcare 4.0 [1]. This paradigm represents a shift toward innovative solutions that leverage advanced technologies to improve healthcare accessibility and efficiency. In this context, the role of semantic technologies is powered by large datasets and algorithms that warrant examination. Semantic technologies highlight the growing adoption of knowledge graphs (KGs)--graph-based data models valued for their ability to represent intricate knowledge structures [2]. KGs are particularlysuited for representing medical data and enhancing the clinical decision-support capabilities of healthcare systems. For instance, [PERSON] et al. [3] demonstrated the potential of health KGs constructed from electronic medical records to improve the organization of complex patient information and enable more accurate diagnostic inferences. Using KGs is aligned with the framework introduced in our previous works [4, 5], designed to assist medical experts in decision making during patient encounters where a medical issue is presented. The framework is iterative by nature, consisting of a series of iterations. In each iteration, the medical expert is prompted with a question (symptom), and the patient responds. Each iteration brings the expert closer to making a decision, ultimately leading to a diagnosis for the issue raised by the patient. The framework operates within a setting that includes, among other constraints, time limitations. This poses the challenge of helping the medical expert refine their decision within a restricted timeframe. Our designed and developed semantic technology-based framework comprises two main components: (a) a KG used to represent medical data and the relationships between various elements [6]. Specifically, symptoms and diseases were represented as nodes, and directed edges linked symptom nodes to disease nodes when a symptom characterized a disease; (b) a set of dedicated algorithms designed to infer questions for the medical expert based on the knowledge captured in the KG and the information provided by the patient during the encounter. At the end of the encounter, the framework's final output is a prioritized list of hypotheses based on the likelihood of accurate diagnosis. Each hypothesis consists of a symptom and a disease. In [5], we enriched the KG by extending it with a symptom ontology [7]. This extension included the addition of new symptoms and hierarchical structures, such that the original symptoms in the KG were augmented with hierarchical constructs rooted in them. These structures incorporated new ontological symptoms, with the relationships within the structure defined as ISA edges. This enrichment also required an extension of the algorithms to support inheritance relationships. This enhancement increased the framework's ability to provide more questions to the medical expert and refine the prioritized hypotheses list. In [5], we presented a full implementation of the framework in Python, including evaluation tests to assess the quality of its outputs. Our generic framework provides a general solution for the iterative nature of patient-expert interactions. In the current work, we advance the framework by addressing specific medical domains. The iterative nature persists in most domains, but domain-specific information can be added to enable the medical expert to make more accurate decisions. To this end, we reinterpreted the term _Strategy_ to describe an extension of the framework incorporating a layer of knowledge and algorithms tailored to a specific medical domain. The following are sample strategies:  
* Healthy lifestyle strategy promotes long-term wellness by incorporating attributes related to nutrition, physical activity, and preventive measures into the KG.