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. 2018 Dec 5:2018:157-165.
eCollection 2018.

A Computable Phenotype for Acute Respiratory Distress Syndrome Using Natural Language Processing and Machine Learning

Majid Afshar  1   2 , Cara Joyce  2 , Anthony Oakey  3 , Perry Formanek  4 , Philip Yang  4 , Matthew M Churpek  5 , Richard S Cooper  2 , Susan Zelisko  6 , Ron Price  6 , Dmitriy Dligach  2   3
Affiliations

A Computable Phenotype for Acute Respiratory Distress Syndrome Using Natural Language Processing and Machine Learning

Majid Afshar et al. AMIA Annu Symp Proc. .

Abstract

Acute Respiratory Distress Syndrome (ARDS) is a syndrome of respiratory failure that may be identified using text from radiology reports. The objective of this study was to determine whether natural language processing (NLP) with machine learning performs better than a traditional keyword model for ARDS identification. Linguistic pre-processing of reports was performed and text features were inputs to machine learning classifiers tuned using 10-fold cross-validation on 80% of the sample size and tested in the remaining 20%. A cohort of 533 patients was evaluated, with a data corpus of 9,255 radiology reports. The traditional model had an accuracy of 67.3% (95% CI: 58.3-76.3) with a positive predictive value (PPV) of 41.7% (95% CI: 27.7-55.6). The best NLP model had an accuracy of 83.0% (95% CI: 75.9-90.2) with a PPV of 71.4% (95% CI: 52.1-90.8). A computable phenotype for ARDS with NLP may identify more cases than the traditional model.

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Figures

Figure 1.
Figure 1.
Traditional model for ARDS identification
Figure 2.
Figure 2.
Discrimination with Area Under the Receiver Operative Characteristic Curve and Calibration plots of NLP model with all radiology reports and Concept Unique Identifier features
See this image and copyright information in PMC

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