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.

项目摘要/摘要 尽管他们很罕见，但罕见疾病在11名美国人中统一影响了一种。罕见病患者 通常面临重大诊断延迟，平均等待6年从症状发作到 准确的诊断。精确医学的最新进展加速了罕见疾病的研究， 压倒性临床医生在临床实践中有效地管理和利用最新知识的能力。 例如，与特发性肺纤维化（IPF）有关的新型基因突变经常出现在 人类基因突变数据库（HGMD）或其他知识库，仅在初始文章中存在。 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低，是许多患者经历过诊断奥德赛的主要原因， 实践。 合作蛋黄酱诊所计划，以解决罕见和未诊断的疾病（praud）与合作伙伴关系 范德比尔特大学医学中心（VUMC），我们旨在通过建立一个新颖的末端来解决翻译差距 端信息框架以加速罕见疾病的诊断。我们计划实现发展 目标1是构建rdaccelerate，这是一种可计算的稀有 疾病知识中心积累并维持有关稀有疾病的最新知识。这是昂贵的 保持文献的最新状态，并以临床证据和经验经验告知。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，例如预训练的语言模型 （PLM）和数据挖掘技术，例如图形神经网络（GNN）嵌入以加速 从潜水员资源范围内提取，集成和采矿。 AIM 2专注于 提供RDRECOMMEND，这是一种培训的深层表型驱动系统 借助Rdaccelerate和纵向患者记录的最新知识。经常 由于稀有性，需要大量时间和精力进行准确的诊断。因此，我们建议申请 各种建议技术来提示罕见疾病鉴别诊断。然后我们将发展 rdConnect，搜索信息的Web门户网站，显示不同的诊断建议并收集 临床证据自动在AIM 3中进行进一步验证。拟议的信息框架将是 通过与临床共同投资者合作的几个实践项目进行评估。这 将使用两种罕见疾病（IPF和 肥大症）。然后，我们将验证框架以支持另外两种罕见疾病（嗜嗜性疾病 综合征[HES]和稀有的肾结石）在扩展到广泛的罕见疾病之前。外部 该解决方案的概括性将通过我们的子现场合作伙伴VUMC进行测试。成功完成 研究将很大 针对现实世界挑战的创新。