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

By Guergana K. Savova, James J. Masanz, P. Ogren, Jiaping Zheng, Sunghwan Sohn, Karin Kipper Schuler, Christopher G. Chute. 2010

2,044 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

Recurrent Neural Networks for Classifying Relations in Clinical Notes (2017, neutral): “, from cTakes [19], MetaMap [20], and GeniaTagger [54]) to recognize modifiers of head words in phrases [13] to address the inconsistent annotation issue.”
Comparing Rule-Based and Deep Learning Models for Patient Phenotyping (2017, background): “These three identify words or phrases within a text and provide the medical concepts they are linked to.(34,46) They significantly reduce the work required from data scientists, who previously had to develop task-specific extractors.”
Development of phenotype algorithms using electronic medical records and incorporating natural language processing (2015, methodology): “21 30-32 Finally, we note that the general phenotyping method presented in this article were successfully applied to a range of defined diseases and conditions but has not been extensively tested on outcomes such as drug response or adverse events.”
A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries (2011, neutral): “are a few of the earliest NLP systems developed for clinical domain. More recently, open source clinical NLP systems such as cTAKES”
Graph-based signal integration for high-throughput phenotyping (2012, methodology): “Automated coding systems such as MetaMap [13], cTAKES [14], and MedLEE [15] can map text to Unified Medical Language System (UMLS) concepts; however, without the addition of customized rules they draw no inferences from it.”
Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review (2017, methodology): “In fact, numerous publicly available NLP systems have been extended to perform the ADE detection task, including MedLEE [47], MetaMap [48], cTAKES [49], MedEx [50], and GATE [51].”
EpiDEA: Extracting Structured Epilepsy and Seizure Information from Patient Discharge Summaries for Cohort Identification (2012, methodology): “The downloadable version of cTAKES was trained on GENIA, Penn TreeBank (PTB), and a corpus derived from the Mayo Clinic Electronic Medical Record (EMR) consisting of 273 clinical notes [8].”
Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review (2023, methodology): “Invenio is built upon an open-source Apache cTAKES platform [41] and analyses unstructured descriptions of medical notes.”
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks (2019, methodology): “Gong et al., for example, uses the clinical Text Analysis Knowledge Extraction System (cTAKES) (Savova et al., 2010) to identify UMLS concept unique identifiers (CUIs) (Bodenreider, 2004) from human-readable feature descriptions, and aggregates features into higher level buckets based on this CUI…”
Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis (2015, methodology): “The latter approach was explored in great detail in Wu et al. [41] and resulted in the implementation of the secondary use Clinical Element Model (CEM) [42] with UIMA, and fully integrated in cTAKES [36] v2.”
A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques (2017, background): “Information extraction is typically considered as a preprocessing step in other biomedical text mining applications such as question answering [10], knowledge extraction [124, 136], hypothesis generation[30, 91] and summarization [65].”
Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts (2011, methodology): “Some of these tools are MedLee, MetaMap, cTakes and HITEx ([29,38,39,40]).”
Text Summarization Techniques: A Brief Survey (2017, background): “Other examples include news websites which produce condensed descriptions of news topics usually as headlines to facilitate browsing or knowledge extractive approaches [8, 61, 72].”
Mining electronic health records: towards better research applications and clinical care (2012, methodology): “The Mayo clinic’s clinical Text Analysis and Knowledge Extraction System (cTAKES) 27 , the Informatics for Integrating Biology and the Bedside (i2b2) HITEX 28 and the long-running Medical Language Extraction and Encoding (MedLEE) 29 system from Columbia University are all examples of NLP-based…”
Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference (2020, neutral): “…to automatically and precisely curate the clinical case reports into structured knowledge, i.e. extract important clinical named entities and relationships from the narratives (Aronson and Lang 2010; Savova et al. 2010; Soysal et al. 2018; Caufield et al. 2019; Alfattni, Peek, and Nenadic 2020).”