An Open Natural Language Processing (NLP) Framework for EHR-based Clinical Research: A Case Demonstration Using the National COVID Cohort Collaborative (N3C)
This paper presents a new framework for using natural language processing to analyze clinical data from electronic health records, particularly in the context of COVID-19 research.
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- 1 Text is a more conventional way than structured data entry to document impressions, clinical findings, assessments, and care plans in the healthcare environment.
- 2 Studies have shown that capturing health information fully in structured format is unlikely, leading to a blended model where physicians use templates and dictate details in text.
- 3 We conducted a case study developing an NLP algorithm for extracting COVID-19 signs and symptoms to demonstrate the framework's viability.
- 4 It is desirable to leverage existing infrastructure to reduce technical burden on the end user.
Introduction
US healthcare institutions have increasingly implemented Electronic Health Record (EHR) systems over the past decade. Healthcare institutions have accumulated and made electronically available large amounts of detailed longitudinal patient information, including lab tests, medications, disease status, and treatment outcomes.
Large clinical databases serve as valuable data sources for clinical and translational research.
Major initiatives, including the CTSA Program’s CD2H/N3C, the eMERGE Network, PCORI’s CRNs, the NIH All of Us Research Program, and the OHDSI Consortia, have been established to exploit this crucial resource.
Small training sets from outside institutions caused difficulties in developing comprehensive rules due to limited representation of features and patterns.
Standardization as a default simplifies adoption for those complying with the standard but cannot be a comprehensive solution.
Methodology
Developing, evaluating, and deploying NLP solutions is a task-specific, iterative, and complex process involving multiple stakeholders. Tables 4, 5, and 6 show the error analysis results for the three sites.
Study Design
Our experiment results showed that a centralized approach is suboptimal for advancing NLP adoption, supporting our proposed federated method.
Error analysis discovered that contexts played an important role in this case study.
Results & Findings
Text is a more conventional way than structured data entry to document impressions, clinical findings, assessments, and care plans in the healthcare environment. Studies have shown that capturing health information fully in structured format is unlikely, leading to a blended model where physicians use templates and dictate details in text.
- Text is a more conventional way than structured data entry to document impressions, clinical findings, assessments, and care plans in the healthcare environment.
- Studies have shown that capturing health information fully in structured format is unlikely, leading to a blended model where physicians use templates and dictate details in.
- Collecting these data requires significant effort to locate, retrieve, and link EHR data into a specific format.
- Creating gold standard corpora requires significant domain expertise and time due to the complexity of clinical language.
- We conducted a case study developing an NLP algorithm for extracting COVID-19 signs and symptoms to demonstrate the framework’s viability.
Text is a more conventional way than structured data entry to document impressions, clinical findings, assessments, and care plans in the healthcare environment.
Studies have shown that capturing health information fully in structured format is unlikely, leading to a blended model where physicians use templates and dictate details in text.
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Practical Applications
A majority of existing clinical NLP studies are done within a monoinstitutional environment, which may suffer from limited external validity and research inclusiveness. Extracting COVID signs/symptoms was not trivial because they could appear as adverse events, instructions, or clinical goals.
Certainty is an attribute of the concept mention including positive, negated, hypothetical, and possible.
A majority of existing clinical NLP studies are done within a monoinstitutional environment, which may suffer from limited external validity and research inclusiveness.
ETL Process Heterogeneity
This section outlines the challenges faced in NLP development due to the variability in EHR systems and the complexities of clinical documentation, which hinder cross-institutional interoperability and reproducibility.
Framework Description
The framework consists of a data ingestion layer, processing layer, and data persistence layer, designed to facilitate NLP development and ensure transparency and interpretability of outcomes.
N3C Case Study
The case study evaluates NLP algorithm performance using data from multiple sites, demonstrating improved F-scores with a multi-site approach compared to a single-site approach, while also discussing error analysis.
Figures Explained
Frequently Asked Questions
Major initiatives, including the CTSA Program’s CD2H/N3C, the eMERGE Network, PCORI’s CRNs, the NIH All of Us Research Program, and the OHDSI Consortia, have been established to exploit this crucial resource. Natural language processing (NLP) has been promoted for its potential to.
Developing, evaluating, and deploying NLP solutions is a task-specific, iterative, and complex process involving multiple stakeholders. We seek to achieve this goal with the OHNLP Collaboratory and have positioned our framework’s workflow to facilitate this task.
Text is a more conventional way than structured data entry to document impressions, clinical findings, assessments, and care plans in the healthcare environment. Studies have shown that capturing health information fully in structured format is unlikely, leading to a blended model where.
A majority of existing clinical NLP studies are done within a monoinstitutional environment, which may suffer from limited external validity and research inclusiveness. A common barrier to NLP adoption is the need to transform input and outputs to conform to an overall.
A majority of existing clinical NLP studies are done within a monoinstitutional environment, which may suffer from limited external validity and research inclusiveness. Small training sets from outside institutions caused difficulties in developing comprehensive rules due to limited representation of features and.
This paper presents a new framework for using natural language processing to analyze clinical data from electronic health records, particularly in the context of COVID-19 research.