Advancing Chronic Condition Symptom Cluster Science Through Use of Electronic Health Records and Data Science Techniques

通过使用电子健康记录和数据科学技术推进慢性病症状群科学

基本信息

批准号：
10394724
负责人：
Theresa Ann Koleck
金额：
$ 23.21万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-01 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10394724
关键词：
18 year old Academic Medical Centers Address Adult Advance Directives American Anxiety Area Award Behavioral Biological Chronic Chronic Obstructive Pulmonary Disease Clinical Clinical Data Clinical Management Clinical assessments Cluster Analysis Code Competence Complement Data Data Analyses Data Science Data Set Data Store Depressed mood Detection Development Diagnosis Dyspnea Electronic Health Record Equipment and supply inventories Ethnic Origin Fatigue Funding Future Genetic Genomics Goals Health Care Costs Health Promotion Healthcare Systems Heart failure Impaired cognition Informatics Intervention Knowledge Knowledge acquisition Laboratories Lead Life Literature Machine Learning Malignant Neoplasms Mastectomy Medical Genetics Medical center Mentorship Modeling Natural Language Processing Nausea Nausea and Vomiting Neurobehavioral Manifestations Non-Insulin-Dependent Diabetes Mellitus Oncology Pain Pathologic Patients Pharmaceutical Preparations Phase Postoperative Nausea and Vomiting Procedures Proteomics Pruritus Public Health Quality of life Race Reproducibility Research Research Personnel Resources Risk Factors Science Scientist Signs and Symptoms Sleep disturbances Strategic Planning Structure Symptoms Techniques Time Training United States National Institutes of Health Universities Validation Woman Work Xerostomia base biomarker discovery biomedical informatics career career development clinical data warehouse clinical practice clinical predictors clinical translation clinically relevant cohort computerized data processing data analysis pipeline data mining data visualization data warehouse electronic data experience health care service utilization health data innovation knowledge translation malignant breast neoplasm patient oriented pre-doctoral process optimization programs sociodemographics statistics symptom cluster symptom management symptom science symptomatic improvement unsupervised learning

项目摘要

Despite their adverse impact on patient quality of life and healthcare utilization and costs, symptom clusters (SCs) in common adult chronic conditions such as cancer, heart failure (HF), type 2 diabetes mellitus (T2DM), and chronic obstructive pulmonary disease (COPD) are understudied and poorly understood. The lack of access to real world, longitudinal patient symptom data sets and inability to adequately model the complexity of SCs has greatly limited research. Based on our previous work, we propose that these gaps can be addressed in an innovative way using electronic health records (EHRs) and data science techniques. Our overall objective is to develop, apply and refine, and implement an optimized data processing and analysis pipeline for the characterization of SCs in common adult chronic conditions for use with EHR data. We hypothesize that a core set of SCs is shared among all common adult chronic conditions and that distinct SCs characterize specific conditions and/or treatments. The long term training goal of this project is to assist Dr. Koleck in becoming an independent investigator conducting a program of research dedicated to mitigating symptom burden in patients with chronic conditions through use of informatics and omics (e.g., genomics and proteomics), the focus of her pre-doctoral work. Using exceptional resources available from Columbia University, the K99 phase of this project will focus on the development of a rigorous pipeline; essential competencies in SC analysis and interpretation; and the data science techniques of clinical data mining, natural language processing, machine learning, and data visualization. In the R00 phase, Dr. Koleck will independently implement the pipeline in another medical center to determine the reproducibility of identified SCs and begin to explore clinical predictors (e.g., socio-demographics, laboratory results, and medications) of SCs. The specific aims are to 1) develop a data-driven pipeline for the characterization of SCs from EHRs using a cohort of adult patients diagnosed with cancer, as SCs have been most systematically characterized in this condition; 2) apply the pipeline to three other common adult chronic conditions that share biological and behavioral risk factors with cancer, i.e., HF, T2DM, and COPD, and evaluate SCs in these conditions; and 3) determine if SCs differ for cancer, HF, T2DM, and COPD when implementing the pipeline within another medical center and explore clinically relevant, EHR- documented predictors of identified SCs. To accomplish research aims and training goals, an interdisciplinary team of scientists with expertise in symptom science, biomedical informatics, data science, pertinent clinical domains, and career development mentorship has been assembled. This research is significant because a pipeline that accommodates the format in which symptom data is already being documented in EHRs has the potential to greatly accelerate the acquisition of SC knowledge and expedite clinical translation of symptom mitigation strategies. Given the array of new competencies to be developed, this K99/R00 award is necessary for achieving the candidate’s career goal of advancing chronic condition symptom science.

尽管它们对患者的生活质量以及医疗保健利用和成本产生不利影响，但症状群 (SC) 常见成人慢性疾病，如癌症、心力衰竭 (HF)、2 型糖尿病 (T2DM)、和慢性阻塞性肺疾病（COPD）尚未得到充分研究和了解。无法访问真实世界的纵向患者症状数据集，并且无法充分建模其复杂性根据我们之前的工作，SC 的研究非常有限，我们建议可以解决这些差距。我们的总体目标是利用电子健康记录 (EHR) 和数据科学技术以创新的方式。是开发、应用、完善和实施优化的数据处理和分析管道常见成人慢性病中 SC 的表征，用于与 EHR 数据一起使用。所有常见成人慢性病共有一组 SC，并且不同的 SC 具有特定的特征该项目的长期培训目标是帮助 Koleck 博士成为一名医生。独立调查员进行一项致力于减轻患者症状负担的研究计划通过使用信息学和组学（例如基因组学和蛋白质组学）来治疗慢性病，这是她的重点博士前工作利用哥伦比亚大学提供的优质资源，完成了该项目的 K99 阶段。项目将重点发展严格的管道分析和能力；解释；以及临床数据挖掘、自然语言处理、机器的数据科学技术在R00阶段，Koleck博士将独立实现管道。另一个医疗中心确定已识别 SC 的可重复性并开始探索临床预测因子 SC 的具体目标（例如，社会人口统计、实验室结果和药物）。数据驱动的管道，使用一组诊断为患有以下疾病的成年患者来表征 EHR 中的 SC 癌症，因为 SC 在这种情况下得到了最系统的表征 2) 将管道应用于三种；其他常见的成人慢性病与癌症具有相同的生物学和行为风险因素，即心力衰竭、 T2DM 和 COPD，并评估这些情况下的 SC；以及 3) 确定 SC 在癌症、心衰、T2DM 方面是否存在差异；和 COPD 在另一个医疗中心实施管道时，并探索临床相关的、EHR- 为了实现研究目标和培训目标，需要记录已识别的 SC 的预测因素。拥有症状科学、生物医学信息学、数据科学、相关临床专业知识的科学家团队这项研究意义重大，因为适应 EHR 中已记录症状数据的格式的管道具有极大加速 SC 知识获取并加快症状临床转化的潜力考虑到需要开发的一系列新能力，K99/R00 奖项是必要的。以实现候选人推进慢性病症状科学的职业目标。

项目成果

期刊论文数量（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Identifying Symptom Information in Clinical Notes Using Natural Language Processing.

使用自然语言处理识别临床记录中的症状信息。

DOI：
发表时间：
2021-05-01
期刊：
影响因子：
2.5
作者：
Koleck, Theresa A;Tatonetti, Nicholas P;Bakken, Suzanne;Mitha, Shazia;Henderson, Morgan M;George, Maureen;Miaskowski, Christine;Smaldone, Arlene;Topaz, Maxim
通讯作者：
Topaz, Maxim

Association Between Health Literacy and Medication Adherence Among Hispanics with Hypertension.

西班牙裔高血压患者健康素养与药物依从性之间的关系。

DOI：
发表时间：
2019
期刊：
影响因子：
0
作者：
Lor, Maichou;Koleck, Theresa A;Bakken, Suzanne;Yoon, Sunmoo;Dunn Navarra, Ann
通讯作者：
Dunn Navarra, Ann

Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review.

电子健康记录自由文本叙述中记录的症状的自然语言处理：系统评价。

DOI：
发表时间：
2019
期刊：
影响因子：
0
作者：
Koleck, Theresa A;Dreisbach, Caitlin;Bourne, Philip E;Bakken, Suzanne
通讯作者：
Bakken, Suzanne

The State of Data Science in Genomic Nursing.

基因组护理数据科学的现状。

DOI：
发表时间：
2020
期刊：
影响因子：
0
作者：
Dreisbach, Caitlin;Koleck, Theresa A
通讯作者：
Koleck, Theresa A

Response to Mental Health of Cardiac Procedure Patients Should Be a Priority for All Healthcare Providers.

对心脏手术患者心理健康的反应应该是所有医疗保健提供者的首要任务。

DOI：
发表时间：
2023-03-01
期刊：
影响因子：
0
作者：
Koleck, Theresa A;Mitha, Shazia;Biviano, Angelo;Caceres, Billy A;Corwin, Elizabeth J;Goldenthal, Isaac;Creber, Ruth Masterson;Turchioe, Megan Reading;Hickey, Kathleen T;Bakken, Suzanne
通讯作者：
Bakken, Suzanne