A Database and Analytics Study of Free Text Clinical Notes and Structured Data to Investigate Phenotype Associations With Outcomes in Patients With COVID-19
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 200
- 试验地点
- 1
- 主要终点
- research database of EHR records from COVID-19 patients processed using NLP tools for named entity recognition and linking adapted to CUH EMR data to identify variables of interest
研究概览
简要总结
A retrospective cohort study investigating clinical notes using Natural Language Processing in combination with structured data from the Electronic Health Record (EHR) to create a database for analytics to identify features associated with outcomes.
详细描述
Patients admitted to Cambridge University Hospitals (CUH)with COVID-19 have undergone routine clinical documentation and specific investigation and testing for COVID-19. The pathway for these patients ranges from supportive measures on the ward to deterioration requiring Intensive therapy Unit (ITU) admission and ventilatory support. Patients are also at risk of developing complications such as Acute Kidney Injury and thromboembolism. Identification of the risk factors for these and other outcomes such as the requirement for ventilation remain a challenge and reviewing the clinical data for these patients is critical in the understanding of the relationship between patient characteristics and outcomes.
There is data available in structured fields in the EHR, however, this is sometimes incomplete and inaccurate. An assessment of the free text clinical notes provides an opportunity to fill in the gaps and provide a much richer dataset for evaluation. We plan to use Natural Language Processing (NLP) (a field of machine learning that allows computers to analyse human language) to review Discharge Summaries of patients admitted to hospital with COVID-19 and convert free text data into structured data for analysis.
The NLP techniques developed by Dr Collier's team include methods for coding of free texts to SNOMED CT and other biomedical ontologies. These methods, based on statistical machine learning from human annotated texts, have been benchmarked for scientific texts and social media. In this project we intend to adapt these techniques for patient records. The techniques will require a number of human annotated patient records in order to adapt. The NLP output will be combined with structured data from the EHR and undergo statistical analysis to identify the rates of complications in patients with COVID-19 and risk factors associated with these. This may help to guide management decisions by earlier intervention to prevent poor outcomes in these patients.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 100 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Male and female
- •Age range: 18 to 100 years
- •Patients admitted to Cambridge University Hospitals with confirmed COVID-19 on lab testing
排除标准
- •Children and patients with a negative COVID test.
结局指标
主要结局
research database of EHR records from COVID-19 patients processed using NLP tools for named entity recognition and linking adapted to CUH EMR data to identify variables of interest
时间窗: 1 year
Our overarching hypothesis is that the NLP-extracted data from the free-text discharge summary can be combined with structured data from the EMR to yield insights into the development of complications. Patient with severe disease requiring ITU admission and non severe disease managed on an inpatient ward will be included. The variables of interest will include patient characteristics and specific encounter related information including length of stay and baseline investigations (e.g., blood tests) and interventions received
次要结局
- A set of annotation guidelines to produce human-expert (gold) labelled data for a subset of the EHR(6 months)
- A comparison of the NLP output to terms in the structured problem list to identify missing terms in the structured problem list(1 year)
研究者
Dr Sapna Trivedi
Doctor
Cambridge University Hospitals NHS Foundation Trust