Learning Precision Medicine for Rare Diseases Empowered by Knowledge-driven Data Mining

项目摘要/摘要 尽管罕见疾病个别罕见，但每11个美国人中就有一个受到这些疾病的共同影响。罕见病患者 通常面临显著的诊断延迟，从出现症状到出现症状平均等待6年 准确的诊断。精准医学的最新进展加速了罕见疾病的研究， 压倒性的临床医生在临床实践中有效管理和利用最新知识的能力。 例如，与特发性肺纤维化(IPF)相关的新基因突变通常不会出现在 人类基因突变数据库(HGMD)或其他知识库，仅出现在最初的文章中。 此外，由于缺乏临床证据和经验知识，对罕见疾病的认识仍然存在 在医疗保健提供者中较低，这是许多患者经历诊断冒险的主要原因， 在实践中。 与梅奥罕见和未诊断疾病临床项目(PRUD)合作 范德比尔特大学医学中心(VUMC)，我们的目标是通过构建一个新的终端来解决翻译差距- 端到端信息学框架，加快罕见病诊断。我们计划实现这一发展 通过三个具体目标对拟议框架进行评估。目标1是构建RDAccelerate，一个可计算的Rare 疾病知识中心，积累和维护有关罕见疾病的最新知识。它的代价是昂贵的 与文献保持同步，并了解临床证据和经验。为了解决这个问题， 我们将利用最新的自然语言处理(NLP)技术，例如预先训练的语言模型 (PLM)和数据挖掘技术，如图神经网络(GNN)嵌入，以加速 从各种资源中提取、集成和挖掘关联。目标2侧重于 提供RDRecommend，这是一个深度表型驱动的系统，用于培训罕见疾病的鉴别诊断 掌握RD方面的最新知识，加速罕见疾病队列的纵向患者记录。它经常 由于这种罕见的情况，要做出准确的诊断需要大量的时间和精力。因此，我们建议将 各种推荐技术，建议罕见疾病的鉴别诊断。然后我们将开发出 RDConnect是一个Web门户，用于搜索信息、显示差异诊断建议和收集 临床证据自动用于AIM 3的进一步验证。拟议的信息学框架将是 通过在普罗德与临床合作研究人员合作的几个实践项目进行评估。这个 将利用两种罕见疾病(IPF和IPF)通过团队科学合作开发框架 肥大细胞增多症)。然后，我们将验证该框架在支持其他两种罕见疾病(高嗜酸性粒细胞增多症)中的有效性 综合症[HES]和罕见肾结石)，然后扩大到一系列罕见疾病。外在的 解决方案的通用性将通过我们的子网站合作伙伴VUMC进行测试。成功完成这项工作 这项研究将具有重要意义，因为它通过技术解决了罕见疾病面临的翻译差距 面对现实世界挑战的创新。

PROJECT SUMMARY/ABSTRACT Despite their individual rarity, rare diseases collectively affect one in eleven Americans. Rare disease patients often face significant diagnostic delays, waiting an average of 6 years from the onset of symptoms to an accurate diagnosis. Recent advances in precision medicine have accelerated research in rare diseases, overwhelming clinicians’ capacities to manage and leverage the latest knowledge efficiently in clinical practice. For example, novel gene mutations related to idiopathic pulmonary fibrosis (IPF) frequently do not appear in the Human Gene Mutation Database (HGMD) or other knowledge bases and are only present in initial articles. Additionally, due to the lack of clinical evidence and empirical knowledge, awareness of rare diseases remains low among healthcare providers and is a major reason for diagnostic odysseys experienced by many patients, in practice. Teaming up Mayo Clinic Program for Rare and Undiagnosed Diseases (PRaUD) with the partnership of Vanderbilt University Medical Center (VUMC), we aim to address the translation gap by building a novel end- to-end informatics framework to accelerate the diagnosis of rare diseases. We plan to achieve the development of the proposed framework through three specific aims. Aim 1 is to construct RDAccelerate, a computable rare disease knowledge hub that accumulates and maintains up-to-date knowledge for rare diseases. It is costly to stay current with the literature and informed with clinical evidence and empirical experience. To address this, we will leverage the latest natural language processing (NLP) techniques such as pre-trained language models (PLMs) and data mining techniques such as graph neural network (GNN) embeddings to accelerate the extraction, integration, and mining of associations from a diverse range of resources. Aim 2 focuses on the provision of RDRecommend, a deep phenotype-driven system for rare disease differential diagnoses trained with the up-to-date knowledge in RDAccelerate and longitudinal patient records of rare disease cohorts. It often takes substantial time and effort for an accurate diagnosis due to the rarity. We therefore propose to apply various recommendation techniques to suggest rare disease differential diagnoses. We will then develop RDConnect, a web portal to search information, display differential diagnostic recommendations, and collect clinical evidence automatically for further validation in Aim 3. The proposed informatics framework will be evaluated through several practice projects at PRaUD in collaboration with clinical co-Investigators. The framework will be developed through team science collaboration using two rare diseases (IPF and mastocytosis). We will then validate the framework in supporting two other rare diseases (hypereosinophilic syndrome [HES] and rare kidney stone) before scaling up to a broad spectrum of rare diseases. The external generalizability of the solution will be tested through our subsite partner VUMC. Successful completion of this study will be significant as it addresses the translational gap faced in rare diseases through technology innovations towards real-world challenges.