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A Methodological Approach to Validate Pneumonia Encounters from Radiology Reports Using Natural Language Processing
AlokSagar Panny1, Harshad Hegde1, Ingrid Glurich1
1Center for Oral-Systemic Health, Marshfield Clinic Research Institute, Marshfield, Wisconsin, United States.
Introduction:
Pneumonia is caused by microbes that establish an infectious process in the lungs. The gold standard for pneumonia diagnosis is radiologist-documented pneumonia-related features in radiology notes that are captured in electronic health records in an unstructured format.
Objective:
The study objective was to develop a methodological approach for assessing validity of a pneumonia diagnosis based on identifying presence or absence of key radiographic features in radiology reports with subsequent rendering of diagnostic decisions into a structured format.
Methods:
A pneumonia-specific natural language processing (NLP) pipeline was strategically developed applying Clinical Text Analysis and Knowledge Extraction System (cTAKES) to validate pneumonia diagnoses following development of a pneumonia feature-specific lexicon. Radiographic reports of study-eligible subjects identified by International Classification of Diseases (ICD) codes were parsed through the NLP pipeline. Classification rules were developed to assign each pneumonia episode into one of three categories: "positive," "negative," or "not classified: requires manual review" based on tagged concepts that support or refute diagnostic codes.
Results:
A total of 91,998 pneumonia episodes diagnosed in 65,904 patients were retrieved retrospectively. Approximately 89% (81,707/91,998) of the total pneumonia episodes were documented by 225,893 chest X-ray reports. NLP classified and validated 33% (26,800/81,707) of pneumonia episodes classified as "Pneumonia-positive," 19% as (15401/81,707) as "Pneumonia-negative," and 48% (39,209/81,707) as "episode classification pending further manual review." NLP pipeline performance metrics included accuracy (76.3%), sensitivity (88%), and specificity (75%).
Conclusion:
The pneumonia-specific NLP pipeline exhibited good performance comparable to other pneumonia-specific NLP systems developed to date.
Insights
A new natural language processing (NLP) pipeline accurately validates pneumonia diagnoses from radiology reports. This method efficiently classifies pneumonia cases, improving diagnostic accuracy in electronic health records.
Area of Science:
- Medical Informatics
- Natural Language Processing
- Pulmonology
Background:
- Pneumonia diagnosis relies on radiologist findings in unstructured electronic health records.
- Current diagnostic methods lack efficient structured data extraction for pneumonia features.
Purpose of the Study:
- To develop a validated methodological approach for pneumonia diagnosis using natural language processing (NLP).
- To convert unstructured radiology report data into structured formats for accurate pneumonia classification.
Main Methods:
- A pneumonia-specific NLP pipeline was created using Clinical Text Analysis and Knowledge Extraction System (cTAKES).
- A lexicon of pneumonia features was developed to parse radiology reports.
- Classification rules were applied to categorize pneumonia episodes as positive, negative, or requiring manual review.
Main Results:
- The NLP pipeline processed 81,707 chest X-ray reports for 91,998 pneumonia episodes.
- It classified 33% as Pneumonia-positive, 19% as Pneumonia-negative, and 48% required manual review.
- Performance metrics showed 76.3% accuracy, 88% sensitivity, and 75% specificity.
Conclusions:
- The developed NLP pipeline demonstrates good performance for validating pneumonia diagnoses.
- This approach is comparable to existing pneumonia-specific NLP systems.
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