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

Phelan‐McDermid syndrome data network: Integrating patient reported outcomes with clinical notes and curated genetic reports

“…Then, the MITRE MIST tool (Aberdeen et al, 2010) and the Scrubber toolkit (McMurry, Fitch, Savova, Kohane, & Reis, 2013) in the Apache cTAKES NLP engine were used to erase Protected Health Information (PHI) elements from the text. Following de‐identification, the Apache cTAKES NLP engine (Savova et al, 2010) was deployed to extract knowledge by identifying occurrences of concepts defined in the Unified Medical Language System (UMLS) (Bodenreider, 2004) in the text. Apache cTAKES also identifies the context in which the concepts are mentioned in the sentence including negation, patient history, family history, and uncertainty.…”

Process Fragment Recognition in Clinical Documents

“…Meystre and Haug propose a NLP-based system to extract medical problems from electronic patient records [14]. Clinical NLP frameworks such as cTAKES, proposed by [17], use it for document indexing and retrieval. It has also given rise to automated semi-and fully-supervised annotation techniques and resources: It has inspired the annotation formats used to build clinical annotated corpora such as the CLEF corpus from [16].…”

Clinical Narrative Analytics Challenges

“…MENELAS can analyze reports in French, English and Dutch. cTAKES, a clinical Text Analysis and Knowledge Extraction System is introduced in [22]. cTAKES is an open-source NLP system that uses rule-based and machine learning techniques to process and extract information to support clinical research.…”

“…According to that publication, this extraction poses new challenges due to the problems mentioned before. The growth in the use of EHRs has generated a significant development in Medical Language Processing systems (MLP), information extraction techniques and applications [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23].…”