Personalized chronic care requires the integration of multimodal health data to enable precise, adaptive, and preventive decision-making. Yet most current digital twin (DT) applications remain organ-specific or tied to isolated data types, lacking a unified and privacy-preserving foundation. This paper introduces the Patient Medical Digital Twin (PMDT), an ontology-driven in silico patient framework that integrates physiological, psychosocial, behavioral, and genomic information into a coherent, extensible model. Implemented in OWL 2.0, the PMDT ensures semantic interoperability, supports automated reasoning, and enables reuse across diverse clinical contexts. Its ontology is structured around modular Blueprints (patient, disease and diagnosis, treatment and follow-up, trajectories, safety, pathways, and adverse events), formalized through dedicated conceptual views. These were iteratively refined and validated through expert workshops, questionnaires, and a pilot study in the EU H2020 QUALITOP project with real-world immunotherapy patients. Evaluation confirmed ontology coverage, reasoning correctness, usability, and GDPR compliance. Results demonstrate the PMDT's ability to unify heterogeneous data, operationalize competency questions, and support descriptive, predictive, and prescriptive analytics in a federated, privacy-preserving manner. By bridging gaps in data fragmentation and semantic standardization, the PMDT provides a validated foundation for next-generation digital health ecosystems, transforming chronic care toward proactive, continuously optimized, and equitable management.

翻译：个性化慢性护理需要整合多模态健康数据，以实现精准、自适应和预防性决策。然而，当前大多数数字孪生应用仍局限于特定器官或孤立的数据类型，缺乏统一且保护隐私的基础框架。本文提出患者医疗数字孪生，这是一种本体驱动的计算机患者框架，将生理、心理社会、行为和基因组信息整合为连贯且可扩展的模型。该框架基于OWL 2.0实现，确保语义互操作性，支持自动化推理，并能在不同临床场景中复用。其本体围绕模块化蓝图（患者、疾病与诊断、治疗与随访、轨迹、安全性、路径及不良事件）构建，并通过专用概念视图进行形式化表达。这些内容通过专家研讨会、问卷调查以及在欧盟H2020 QUALITOP项目中针对真实免疫治疗患者开展的试点研究进行迭代优化与验证。评估结果证实了本体的覆盖度、推理正确性、可用性及符合GDPR要求。研究结果表明，PMDT能够统一异构数据，将能力问题可操作化，并以联邦化且保护隐私的方式支持描述性、预测性和规范性分析。通过弥合数据碎片化与语义标准化之间的鸿沟，PMDT为下一代数字健康生态系统提供了经过验证的基础框架，推动慢性护理向主动预防、持续优化和公平管理的模式转型。