Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications

TLDR

cTAKES builds on UIMA and OpenNLP and provides foundational annotations for higher‑level semantic processing of clinical free text. The authors aim to build and evaluate an open‑source NLP system, cTAKES, for extracting information from clinical free text. cTAKES comprises domain‑trained components that generate rich linguistic and semantic annotations. cTAKES attains component accuracies above 0.92, system‑level F‑scores of 0.715 exact and 0.824 overlapping, and concept‑mapping, negation, and status accuracies between 0.58 and 0.96, with overall performance assessed across five applications.

Abstract

We aim to build and evaluate an open-source natural language processing system for information extraction from electronic medical record clinical free-text. We describe and evaluate our system, the clinical Text Analysis and Knowledge Extraction System (cTAKES), released open-source at http://www.ohnlp.org. The cTAKES builds on existing open-source technologies—the Unstructured Information Management Architecture framework and OpenNLP natural language processing toolkit. Its components, specifically trained for the clinical domain, create rich linguistic and semantic annotations. Performance of individual components: sentence boundary detector accuracy=0.949; tokenizer accuracy=0.949; part-of-speech tagger accuracy=0.936; shallow parser F-score=0.924; named entity recognizer and system-level evaluation F-score=0.715 for exact and 0.824 for overlapping spans, and accuracy for concept mapping, negation, and status attributes for exact and overlapping spans of 0.957, 0.943, 0.859, and 0.580, 0.939, and 0.839, respectively. Overall performance is discussed against five applications. The cTAKES annotations are the foundation for methods and modules for higher-level semantic processing of clinical free-text.