# A Strategy-Driven Semantic Framework for Precision Decision Support in Targeted Medical Fields
## 1 Introduction
By leveraging patient-specific data, the framework can suggest personalized recommendations associated with the patient's health goals and lifestyle. * Mental Health Support Strategy integrates mental health assessments and decision-making tools to assist in diagnosing and treating psychological conditions. * Emergency strategy prioritizes rapid and accurate decision making under time-critical conditions. It incorporates attributes such as symptom severity into the KG, enabling medical experts to identify potential diagnoses quickly. Each proposed strategy retains the iterative nature of the medical decision-making process while incorporating distinct goals and additional knowledge layers. These layers are implemented by adding new attributes to the KG nodes and edges, supported by algorithms that refine the framework's generated hypotheses. The structure of the hypotheses may also change according to the needs of the specific medical domain. This paper presents the concept of strategy through a case study of _emergency strategy_. This strategy simulates an emergency room environment, focusing on promptly identifying emergency conditions despite time-constrained communication between patients and medical experts. The goal of this strategy is to identify life-threatening diseases and save lives. To determine the essential characteristics of the strategy, we conducted interviews with two physicians, which revealed the following key insights: (1) physicians often identify additional symptoms during physical examinations or through abnormal vital signs not initially reported by patients; (2) personal patient data, such as age, gender, pre-existing conditions, and medications, play a critical role in the diagnostic process; (3) diseases are categorized as life-threatening or non-life-threatening based on symptoms and patient information; and (4) the strategy prioritizes the prompt elimination of life-threatening conditions as a primary objective. This enhancement enriches the KG with semantic conditional edges reflecting patient-specific indicators, such as age, gender, and pre-existing conditions, while refining the framework's algorithms to prioritize excluding life-threatening diseases. The hypotheses are generated through an inference process that identifies symptoms to either confirm or rule out critical conditions. Simulating an emergency room environment, this enhancement enables the framework to focus on rapidly identifying life-threatening conditions in time-sensitive settings. Building on these findings, the architecture of our framework has been enhanced to provide medical experts with a prioritized list of hypotheses focusing on life-threatening diseases. This enhancement involved enriching the KG with conditional edges that incorporate patient-specific indicators, such as age, gender, and pre-existing conditions, and refining the framework's algorithms to emphasize the exclusion of life-threatening conditions. The hypotheses are generated through an inference process to identify symptoms that confirm or rule out critical diseases. By simulating emergency room scenarios, this enhancement enables the framework to prioritize the rapid identification of critical conditions in time-sensitive settings. This strategy-based approach is both innovative and practical. By preserving the core iterative process and integrating domain-specific strategies, the framework remains adaptable across diverse medical domains while retaining its foundational strengths. The simplicity of the KG structure, combined with its ability to incorporate new attributes, enhances both the scalability and flexibility. The paper is organized as follows: Section 2 discusses knowledge representation and reviews studies that utilize KGs for healthcare applications. Section 3 details our framework, and Section 4 describes the framework extension via tailored strategies for enhanced decision making. Section 5 describes in detail the case study of emergency strategy.