Using natural language processing and machine learning to identify potentially preventable hospital admissions among outpatients with chronic lung diseases

项目摘要 患有慢性肺部疾病（CLD）的患者经常因潜在的可预防疾病而入院， 可使.这样的入院可能与患者偏好不一致和/或代表低价值分配， 卫生系统资源。为了预测这样的入院，该领域中现有的临床预测模型通常 产生一个“全因”风险估计，即使准确，忽略了背后的可操作机制 入院风险，因此未能确定规定的反应。这种局限性可以解释唯一适度的--充其量 - 在大多数经过测试的干预措施中， 在这个人群中。因此，有机会预测住院风险，同时确定 患者表型（即，社会、人口统计学、临床和其他特征的一些集合）， 存在已知的预防性干预。拟议的研究旨在克服这些局限性， 通过（1）对CLD住院患者进行半结构化访谈， 护理人员和临床医生，直接识别可改变的风险及其相关的表型， （2）使用自然语言处理技术（NLP）构建分类模型， 在临床就诊记录的非结构化文本中提供细致入微的叙述、社会和临床信息， （3）建立风险预测模型，重点关注具有这些表型的可操作表型， 一系列传统的回归和机器学习方法，同时也结合了大量的 预测变量的文本数据和会计随时间变化的趋势。候选人的前期工作 使用基本的NLP技术来显著提高住院患者临床预测模型的区分度 人口的增长推动了这一方法论。住院治疗的负担和费用不断增加， 与CLDs，以及越来越多的关注，从联邦付款人，突出了这项工作的关键性质。完成 这项研究将建立在候选人过去的培训，其中包括卫生政策科学硕士 获得NHLBI T32支持的研究，并将提供经验，教育和指导， 成为一名完全独立的调查员。根据候选人量身定制的培训计划，他将 通过课程学习，获得混合方法研究，NLP和试验设计方面的高级技能，关闭 指导和监督，以及直接实践。这些技能将使他能够理想地提交成功的R 01测试 在现实世界环境中部署所提出的临床预测模型。候选人的主要导师， 合作者和顾问将确保遵守拟议的时间轴和目标，并提供 支持性环境，使他能够发展独立的研究事业，测试临床应用的实际部署。 预测模型，以减少CLD患者的低价值和偏好不一致的护理。

Project Summary Patients living with chronic lung diseases (CLDs) are frequently admitted to the hospital for potentially preventable causes. Such admissions may be discordant with patient preferences and/or represent a low-value allocation of health system resources. To anticipate such admissions, existing clinical prediction models in this field typically produce an “all-cause” risk estimate which, even if accurate, overlooks the actionable mechanisms behind admission risk and therefore fails to identify a prescribed response. This limitation may explain the only modest – at best – reductions in hospital admissions and readmissions seen in most intervention bundles that have been tested in this population. An opportunity exists, therefore, to predict hospitalization risk while simultaneously identifying patient phenotypes (i.e. some constellation of social, demographic, clinical, and other characteristics) for which known preventive interventions exist. The proposed study seeks to overcome these limitations and capitalize on this opportunity by (1) conducting semi-structured interviews with hospitalized patients with CLDs, and their caregivers and clinicians, to directly identify modifiable risks and their associated phenotypes driving hospital admissions; (2) using natural language processing techniques (NLP) to build classification models that will leverage nuanced narrative, social, and clinical information in the unstructured text of clinical encounter notes to identify patients with these phenotypes; and (3) building risk prediction model focused on actionable phenotypes with a wide-array of traditional regression and machine learning approaches while also incorporating large numbers of predictor variables from text data and accounting for time-varying trends. The candidate's preliminary work using basic NLP techniques to significantly improve the discrimination of clinical prediction models in an inpatient population has motivated this methodologic approach. The rising burden and costs of hospitalizations associated with CLDs, and the increasing attention from federal payers, highlights the critical nature of this work. Completion of this research will build upon the candidate's past training, which includes a Masters of Science in Health Policy Research obtained with NHLBI T32 support, and will provide the experience, education, and mentorship to allow the candidate to become a fully independent investigator. Based on the candidate's tailored training plan, he will acquire advanced skills in mixed-methods research, NLP, and trial design all through coursework, close mentoring and supervision, and direct practice. The skills will position him ideally to submit successful R01s testing the deployment of the proposed clinical prediction models in real-world settings. The candidate's primary mentor, collaborators, and advisors will ensure adherence to the proposed timeline and goals and provide a supportive environment for him to develop an independent research career testing the real-world deployment of clinical prediction models to reduce low-value and preference-discordant care for patients with CLDs.