Dataset Information

A Methodological Approach to Validate Pneumonia Encounters from Radiology Reports Using Natural Language Processing.

ABSTRACT:

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.

SUBMITTER: Panny A

PROVIDER: S-EPMC9391313 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Publications

A Methodological Approach to Validate Pneumonia Encounters from Radiology Reports Using Natural Language Processing.

Panny AlokSagar A Hegde Harshad H Glurich Ingrid I Scannapieco Frank A FA Vedre Jayanth G JG VanWormer Jeffrey J JJ Miecznikowski Jeffrey J Acharya Amit A

Methods of information in medicine 20220405 1-02

<h4>Introduction</h4>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.<h4>Objective</h4>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 ...[more]

PMID: 35381617

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Dataset Information

A Methodological Approach to Validate Pneumonia Encounters from Radiology Reports Using Natural Language Processing.

Introduction

Objective

Methods

Results

Conclusion

Publications

A Methodological Approach to Validate Pneumonia Encounters from Radiology Reports Using Natural Language Processing.

Similar Datasets

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