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Computerized Text Analysis to Enhance Automated Pneumonia Detection
Authors:Sylvain DeLisle  Tariq Siddiqui  Adi Gundlapalli  Matthew Samore  Leonard D’Avolio
Institution:1.VA Maryland Health Care System, Baltimore, MD, USA;;2.Medicine, University of Maryland, Baltimore, MD, USA;;3.VA Salt Lake City Health Care System, Salt Lake City, UT, USA;;4.University of Utah, Salt Lake City, UT, USA;;5.VA Boston Health Care System, Boston, MA, USA;;6.Harvard Medical School, Boston, MA, USA
Abstract:

Objective

To improve the surveillance for pneumonia using the free-text of electronic medical records (EMR).

Introduction

Information about disease severity could help with both detection and situational awareness during outbreaks of acute respiratory infections (ARI). In this work, we use data from the EMR to identify patients with pneumonia, a key landmark of ARI severity. We asked if computerized analysis of the free-text of clinical notes or imaging reports could complement structured EMR data to uncover pneumonia cases.

Methods

A previously validated ARI case-detection algorithm (CDA) (sensitivity, 99%; PPV, 14%) 1] flagged VAMHCS outpatient visits with associated chest imaging (n = 2737). Manually categorized imaging reports (Non-Negative if they could support the diagnosis of pneumonia, Negative otherwise; kappa = 0.88), served as a reference for the development of an automated report classifier through machine-learning 2]. EMR entries related to visits with Non-Negative chest imaging were manually reviewed to identify cases with Possible Pneumonia (new symptom(s) of cough, sputum, fever/chills/night sweats, dyspnea, pleuritic chest pain) or with Pneumonia-in-Plan (pneumonia listed as one of two most likely diagnoses in a physician’s note). These cases were used as reference for the development of the EMR-based CDAs. CDA components included ICD-9 codes for the full spectrum of ARI 1] or for the pneumonia subset, text analysis aimed at non-negated ARI symptoms in the clinical note 1] and the above-mentioned imaging report text classifier.

Results

The manual review identified 370 reference cases with Possible Pneumonia and 250 with Pneumonia-in-Plan. Statistical performance for illustrative CDAs that combined structured EMR parameters with or without text analyses are shown in the ConclusionsAutomated text analysis of chest imaging reports can improve our ability to separate outpatients with pneumonia from those with a milder form of ARI.
CDA Number123456789101112
Possible PneumoniaPneumonia-in-Plan
CDA Components
(Pneumonia ICD-9 Codes
(ARI ICD-9 Codes
OR Text of Clinical Notes)
AND Chest Imaging Obtained
AND Text of Imaging Reports
Sensitivity (%)36.828.485.958.499.766.25240.893.668.810074.8
Specificity (%)95.499.729.898.52.29895.499.629.896.82.395.7
PPV (%)55.393.81686.113.783.352.891.11268.59.363.6
NPV (%)919093.293.898.19595.294.4989710097.4
F-Measure44.243.62769.624.173.852.456.42168.61768.7
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Keywords:situational awareness  influenza  surveillance  electronic medical record  pneumonia
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