Improved Disease Stratification Using Electronic Health Records

摘要 这份职业发展申请描述了有针对性的课程工作和指导研究 在使用疾病电子健康记录数据方面的独立研究进展 子类型法。电子健康记录已显示出作为可扩展来源的巨大前景 为生物医学研究提供数据，使“精准医学”成为可能。自然语言处理的使用 技术使在自由文本临床笔记中找到的特定术语的计算分析成为可能。 从临床记录中提取症状信息的能力的提高将会改善 研究人员使用患者记录中未识别的数据发现疾病的能力 子类型。与症状相关的术语在心理健康的背景下特别重要，但 与疾病或药品名称等其他术语相比，笔记中也更难检测到。 该方案的研究目标为可伸缩扩展提供了一种新的方法 生物医学术语和这些术语及其修饰语的改进检测(例如， 有家族性，缺席)。可以使用这些增强方法提取的更丰富的数据集是 然后用来定义患者队列并检测疾病亚型和反应的预测因素 特定的药物干预。 将得到的患者分层与没有丰富数据的分组进行比较 并在独立的数据集上进行了验证。这项工作的首要假设是 增强的临床笔记挖掘将使统计意义重大和临床相关 精神障碍的基于症状的分层。为了检验这一假设，我将： 目标1：开发特定于领域的术语扩展的半自动管道 目标2：通过使用增强型术语提取对患者队列进行定义和分层 目标3：评价通过目标1和目标2获得的更丰富的数据集的有效性和实用性 一个最需要更多以证据为基础的疾病分层的领域，也是最 挑战的原因有很多，那就是心理健康。精神健康障碍占了 全世界30%的非致命性疾病造成了负担，并造成了数万亿美元的经济负担 还有攀登。此外，心理健康症状通常是主观的和自我报告的， 几乎没有客观可衡量的迹象。这项提议的影响是它将极大地改善 我们能够使用电子病历数据对这一服务严重不足的卫生和医疗领域的患者进行分层 医疗保健。 本项目的主要创新之处在于改编和应用了半监督的 增加心理健康术语的模式学习管道，以及一种新的方法 使用显著未充分利用的生物医学数据来源(即临床数据)进行疾病分层 笔记。 这项工作解决了在快速发展的生物医学中挖掘临床笔记的主要挑战 并利用大量尚未开发和利用的有价值的医学证据来源 没有得到充分利用。这些加强临床记录使用的方法加在一起，将使识别 使用EHR中闲置的数据对不同的患者亚组进行分析。

ABSTRACT This Career Development Application describes targeted coursework and mentored research for progression to independent research in the use of electronic health record data for disease subtyping. Electronic health records have demonstrated great promise as a scalable source of data for biomedical research to enable “precision medicine.” Use of natural language processing techniques has enabled computational analysis of specific terms found in free text clinical notes. An improved ability to extract symptom information from clinical notes would improve researchers’ ability to use de-identified data from patient records for discovery of disease subtypes. Symptom-related terms are particularly important in the context of mental health, but also harder to detect in notes than other terms like diseases or drug names. The research aims of this proposal present a novel approach to scalable extension of biomedical terminologies and improved detection of those terms and their modifiers (e.g. severe, familial, absent). The richer dataset that can be extracted using these enhanced approaches is then used to define patient cohorts and to detect disease subtypes and predictors of response to specific pharmaceutical intervention. Resulting patient stratification will be compared to groupings made without the enriched data and validated on an independent data set. The overarching hypothesis of this work is that enhanced mining of clinical notes will enable statistically significant and clinically relevant symptom-based stratification of psychiatric disorders. In order to test this hypothesis, I will: Aim 1: Develop a semi-automated pipeline for domain-specific terminology extension Aim 2: Define and stratify patient cohorts through use of enhanced term extraction Aim 3: Evaluate the validity and utility of the richer set of data obtained through Aims 1 and 2 One area of greatest need for more evidence-based disease stratification, and also of greatest challenge for a number of reasons, is that of mental health. Mental health disorders account for 30% of non-fatal disease burden world-wide, and pose an economic burden of trillions of dollars and climbing. Moreover, mental health symptoms are generally subjective and self-reported, with few objectively measurable signs. The impact of this proposal is that it will dramatically improve our ability to use EHR data to stratify patients in this drastically underserved area of health and healthcare. The major innovations of this project are the adaptation and application of a semi-supervised pattern learning pipeline to augment mental health terminologies, and a novel approach to disease stratification using a significantly underutilized source of biomedical data, namely clinical notes. This work addresses a major challenge for mining clinical notes in rapidly evolving biomedical domains and leverages a valuable source of medical evidence that is largely untapped and underutilized. Together, these methods for enhanced use of clinical notes will enable identification of distinct patient subgroups using data that is sitting idle in EHRs.