FastContext: an efficient and scalable implementation of the ConText algorithm

TL;DR

FastContext is a new implementation of the ConText algorithm that significantly improves processing speed and scalability for large-scale clinical text analysis, while also enhancing accuracy with more rules.

Contribution

It introduces FastContext, a rule-hashing based implementation that outperforms existing Java versions in speed and scalability for clinical NLP tasks.

Findings

FastContext is two orders of magnitude faster than JavaConText and GeneralConText.

FastContext's accuracy improves with more rules, unlike other implementations.

FastContext is suitable for processing very large clinical corpora efficiently.

Abstract

Objective: To develop and evaluate FastContext, an efficient, scalable implementation of the ConText algorithm suitable for very large-scale clinical natural language processing. Background: The ConText algorithm performs with state-of-art accuracy in detecting the experiencer, negation status, and temporality of concept mentions in clinical narratives. However, the speed limitation of its current implementations hinders its use in big data processing. Methods: We developed FastContext through hashing the ConText's rules, then compared its speed and accuracy with JavaConText and GeneralConText, two widely used Java implementations. Results: FastContext ran two orders of magnitude faster and was less decelerated by rule increase than the other two implementations used in this study for comparison. Additionally, FastContext consistently gained accuracy improvement as the rules increased (the desired outcome of adding new rules), while the other two implementations did not. Conclusions: FastContext is an efficient, scalable implementation of the popular ConText algorithm, suitable for natural language applications on very large clinical corpora.