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

通过知识驱动的数据挖掘学习罕见疾病的精准医学

• 批准号: 10732934
• 负责人: HONGFANG LIU
• 金额: $ 72.37万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-06 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10732934
• 关键词: Academic Medical Centers; Acceleration; Address; Affect; American; Asthma; Awareness; Cardiovascular Diseases; Characteristics; Chronic Obstructive Pulmonary Disease; Clinic; Clinical; Collaborations; Consumption; Data; Data Analyses; Databases; Development; Diagnosis; Diagnostic; Differential Diagnosis; Disease; Disseminated eosinophilic collagen disease; Electronic Health Record; Face; Feedback; Gene Mutation; Genomic medicine; Goals; Graph; Growth; Health Personnel; Heart Diseases; Human; Individual; Informatics; Information Management; Information Resources; Kidney Calculi; Knowledge; Knowledge Discovery; Learning; Literature; Manuals; Medical Records; Methods; Mining; Modeling; Natural Language Processing; Patients; Phenotype; Physicians; Play; Rare Diseases; Recommendation; Records; Research; Research Personnel; Resources; Respiratory Disease; Role; Science; Semantics; Source; Symptoms; System; Techniques; Testing; Time; Training; Translations; Validation; accurate diagnosis; biomedical informatics; clinical practice; cohort; cost; data mining; empowerment; experience; graph neural network; idiopathic pulmonary fibrosis; improved; informatics infrastructure; interest; knowledge base; knowledge hub; language training; mRNA Differential Displays; mastocytosis; novel; pragmatic implementation; precision medicine; programs; scale up; technological innovation; text searching; tool; web based interface; web portal

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.

项目总结/摘要 尽管罕见病个别罕见，但罕见病共同影响了十一分之一的美国人。罕见病患者 通常面临严重的诊断延误，从症状出现到确诊，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，仅在初始文章中出现。 此外，由于缺乏临床证据和经验知识， 在医疗保健提供者中较低，并且是许多患者经历诊断困难的主要原因， 在实践中 马约诊所罕见和未诊断疾病项目（PRaUD）与以下合作伙伴合作： 在范德比尔特大学医学中心（VUMC），我们的目标是通过建立一个新颖的终端来解决翻译差距 这是一个全面的信息学框架，以加速罕见疾病的诊断。我们计划实现发展 通过三个具体目标来实现拟议框架。目标1是构建RDAccelerate，一个可计算的稀有 疾病知识中心，积累和维护罕见疾病的最新知识。这是昂贵的， 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，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、整合和挖掘关联。目标2侧重于 提供RDRecommend，这是一个经过培训的用于罕见疾病鉴别诊断的深度表型驱动系统 利用RDAccelerate和罕见病队列纵向患者记录中的最新知识。它经常 由于罕见，需要大量的时间和精力来进行准确的诊断。因此，我们建议 各种推荐技术，以建议罕见疾病的鉴别诊断。然后我们将开发 RDConnect，一个用于搜索信息、显示鉴别诊断建议并收集 临床证据自动用于目标3中的进一步验证。拟议的信息学框架将 与临床合作研究者合作，通过PRaUD的几个实践项目进行评价。的 将通过团队科学合作，使用两种罕见疾病（IPF和 肥大细胞增多症）。然后，我们将验证支持其他两种罕见疾病（嗜酸性粒细胞增多症）的框架 综合征[HES]和罕见肾结石），然后扩大到广泛的罕见疾病。外部 该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。成功完成本 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。 面对现实世界的挑战。