慢性病护理中的患者数字双胞胎：技术障碍、经验教训与未来之路
Patient Digital Twins for Chronic Care: Technical Hurdles, Lessons Learned, and the Road Ahead
AI总结：
本文探讨了患者医疗数字双胞胎在慢性病护理中的技术挑战与未来发展方向，提出了基于本体驱动建模和联邦分析的实施方法，并强调了标准对齐、隐私治理和多模态推理等关键技术。
摘要：
慢性疾病是全球疾病、死亡和医疗成本的主要负担，但当前的医疗系统仍然碎片化且主要以反应性为主。患者医疗数字双胞胎（PMDTs）提供了一种范式转变：整合临床、基因组、生活方式和生活质量数据的全面、持续更新的患者数字代表。我们通过基于本体驱动建模和联邦分析试点报告了PMDTs的早期实施。来自QUALITOP肿瘤学研究和分布式AI平台的见解证实了可行性和挑战：与HL7 FHIR和OMOP标准对齐、嵌入隐私治理、扩展联邦查询和设计直观的临床人员界面。我们还突出了技术进展，如多模态蓝图上的自动推理和患者结果的预测分析。通过反思这些经验，我们为软件工程师提出了可行见解，并指出了机会，如DSLs和模型驱动工程，以推动PMDTs向可信、适应性的慢性病护理生态系统发展。
英文摘要：
Chronic diseases constitute the principal burden of morbidity, mortality, and healthcare costs worldwide, yet current health systems remain fragmented and predominantly reactive. Patient Medical Digital Twins (PMDTs) offer a paradigm shift: holistic, continuously updated digital counterparts of patients that integrate clinical, genomic, lifestyle, and quality-of-life data. We report early implementations of PMDTs via ontology-driven modeling and federated analytics pilots. Insights from the QUALITOP oncology study and a distributed AI platform confirm both feasibility and challenges: aligning with HL7 FHIR and OMOP standards, embedding privacy governance, scaling federated queries, and designing intuitive clinician interfaces. We also highlight technical gains, such as automated reasoning over multimodal blueprints and predictive analytics for patient outcomes. By reflecting on these experiences, we outline actionable insights for software engineers and identify opportunities, such as DSLs and model-driven engineering, to advance PMDTs toward trustworthy, adaptive chronic care ecosystems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
