Development of a portable tool to identify patients with atrial fibrillation using clinical notes from the electronic medical record

Rashmee U. Shah*, R. Kannan Mutharasan, Faraz S. Ahmad, Anna G. Rosenblatt, Hawkins C. Gay, Benjamin A. Steinberg, Mark Yandell, Martin Tristani-Firouzi, Jake Klewer, Rebeka Mukherjee, Donald M. Lloyd-Jones

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background: The electronic medical record contains a wealth of information buried in free text. We created a natural language processing algorithm to identify patients with atrial fibrillation (AF) using text alone. Methods and Results: We created 3 data sets from patients with at least one AF billing code from 2010 to 2017: a training set (n=886), an internal validation set from site no. 1 (n=285), and an external validation set from site no. 2 (n=276). A team of clinicians reviewed and adjudicated patients as AF present or absent, which served as the reference standard. We trained 54 algorithms to classify each patient, varying the model, number of features, number of stop words, and the method used to create the feature set. The algorithm with the highest F-score (the harmonic mean of sensitivity and positive predictive value) in the training set was applied to the validation sets. F-scores and area under the receiver operating characteristic curves were compared between site no. 1 and site no. 2 using bootstrapping. Adjudicated AF prevalence was 75.1% at site no. 1 and 86.2% at site no. 2. Among 54 algorithms, the best performing model was logistic regression, using 1000 features, 100 stop words, and term frequency-inverse document frequency method to create the feature set, with sensitivity 92.8%, specificity 93.9%, and an area under the receiver operating characteristic curve of 0.93 in the training set. The performance at site no. 1 was sensitivity 92.5%, specificity 88.7%, with an area under the receiver operating characteristic curve of 0.91. The performance at site no. 2 was sensitivity 89.5%, specificity 71.1%, with an area under the receiver operating characteristic curve of 0.80. The F-score was lower at site no. 2 compared with site no. 1 (92.5% [SD, 1.1%] versus 94.2% [SD, 1.1%]; P<0.001). Conclusions: We developed a natural language processing algorithm to identify patients with AF using text alone, with >90% F-score at 2 separate sites. This approach allows better use of the clinical narrative and creates an opportunity for precise, high-throughput cohort identification.

Original languageEnglish (US)
Pages (from-to)772-778
Number of pages7
JournalCirculation: Cardiovascular Quality and Outcomes
DOIs
StateAccepted/In press - 2020

Keywords

  • algorithm
  • artificial intelligence
  • atrial fibrillation
  • electronic medical record
  • natural language processing
  • prevalence

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

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