# A Strategy-Driven Semantic Framework for Precision Decision Support in Targeted Medical Fields
## 6 Discussion and Future Work  
Knowledge representation and reasoning techniques assist doctors in decision making and inferring new information about diseases from previously represented data [30]. The current work presented in this paper introduces a new concept, which we have termed \"strategy.\" A strategy extends the existing framework designed to address specific needs in various medical domains. On the one hand, the strategy continues to implement the framework's core functionality, which supports the iterative nature of interactions between the patient and the medical expert. On the other hand, it enables adding a layer representing a specific medical domain through attributes added to the KG, supported by dedicated algorithms. In the case study we presented, some attributes are formulated as Boolean rules attached to the graph's edges. During runtime, an instance of the KG is constructed for the patient interacting with the medical expert. Each edge associated with a Boolean rule undergoes evaluation, yielding either TRUE or FALSE based on the patient's data. If the rule evaluates to TRUE, the edge is included in the patient's graph; otherwise, it is excluded. Building upon the foundational framework, the strategy concept extends its capabilities to address domain-specific needs in diverse medical contexts. Strategies leverage the KG's flexibility to introduce specialized attributes and relationships tailored to specific healthcare challenges. Each strategy is designed to enhance the framework's capacity for precise context-aware decision support while preserving the iterative nature of the decision-making process. The emergency strategy is a case study focusing on rapidly identifying life-threatening diseases. Building on the work by [PERSON] et al. (2018), who demonstrated the effectiveness of machine learning in sepsis clinical decision support, our approach integrates an emergency strategy layer into the KG-based decision-making framework. This enhancement prioritizes life-threatening conditions, improving decision-making efficiency and patient outcomes in urgent scenarios. We advanced clinical decision making by developing a KG enriched with conditional edges informed by patient-specific data, enabling real-time adaptation to individual profiles. Additionally, we refined existing algorithms to seamlessly incorporate the emergency strategy, creating a diagnostic process that is both precise and responsive to critical clinical demands. These advancements establish a more adaptable framework for emergency contexts, providing a strong foundation for future innovations in medical diagnostics. Future efforts will focus on validating the emergency strategy with real-world clinical data to assess its practical effectiveness in supporting healthcare professionals. Additionally, we plan to refine the logic for conditional edges by incorporating continuous data, enabling more granular inferences and improved diagnostic accuracy. Expanding on this work, we aim to integrate machine learning models capable of dynamically updating the KG with incoming data, enhancing the framework's adaptability to evolving clinical practices and diverse patient populations. The strategy concept offers potential for scalability across other medical domains requiring rapid and precise decision making, such as cardiology or oncology. Furthermore, addressing ethical considerations, such as mitigating bias and ensuring data privacy, will be a critical focus as we integrate machine learning into the framework. Collaborations with clinical practitioners and institutions will also play a pivotal role in co-developing and validating these innovations in real-world scenarios. Conceptualization, S.A.-K. and D.B. ; methodology, D.B. and S.A.-K.; validation, S.A.-K. and D.B. ; formal analysis, S.A.-K. and D.B. ; investigation, S.A.-K. and D.B; data curation, D.B. and S.AK; writing--original draft preparation, D.B. and S.A.-K.; writing--review and editing, D.B. and S.A.-K.; visualization, S.A.-K. and D.B.