Natural Language Processing (NLP) Analysis of Free Text Notes to Investigate Coronavirus (COVID-19)
A Database and Analytics Study of Free Text Clinical Notes and Structured Data to Investigate Phenotype Associations With Outcomes in Patients With COVID-19
1 other identifier
observational
200
1 country
1
Brief Summary
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Jul 2020
Shorter than P25 for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
June 15, 2020
CompletedFirst Posted
Study publicly available on registry
June 16, 2020
CompletedStudy Start
First participant enrolled
July 1, 2020
CompletedPrimary Completion
Last participant's last visit for primary outcome
July 1, 2021
CompletedStudy Completion
Last participant's last visit for all outcomes
July 1, 2021
CompletedJuly 28, 2021
July 1, 2021
1 year
June 15, 2020
July 22, 2021
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
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
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
1 year
Secondary Outcomes (2)
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
Eligibility Criteria
Patients admitted to Cambridge University Hospitals NHS Foundation Trust with confirmed COVID-19
You may qualify if:
- Male and female
- Age range: 18 to 100 years
- Patients admitted to Cambridge University Hospitals with confirmed COVID-19 on lab testing
You may not qualify if:
- Children and patients with a negative COVID test.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Cambridge University NHS Foundation Trust
Cambridge, United Kingdom
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Design
- Study Type
- observational
- Observational Model
- CASE ONLY
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Doctor
Study Record Dates
First Submitted
June 15, 2020
First Posted
June 16, 2020
Study Start
July 1, 2020
Primary Completion
July 1, 2021
Study Completion
July 1, 2021
Last Updated
July 28, 2021
Record last verified: 2021-07