A common type system for clinical natural language processing

Wu, Stephen; Kaggal, Vinod C.; Dligach, Dmitriy; Masanz, James; Chen, Pei; Becker, Lee B.; Chapman, Wendy W.; Savova, Guergana; Liu, Hongfang; Chute, Christopher G.

doi:10.1186/2041-1480-4-1

Cited by 33 publications

(19 citation statements)

References 14 publications

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“…linguistic and clinical data-for reasoning over the whole EHR in a computationally actionable way. This is the case of Wu et al (2013) and Tao et al (2013), who used a higher-level formal (OWL) clinical EHR representation implemented in cTakes, but relying on a low-level annotation (Savova et al 2012). The Biological Expression Language (BEL) 9 seems to be a mix between the low and high-level of annotation for life science text (vs. clinical).…”

Section: Introductionmentioning

confidence: 99%

A French clinical corpus with comprehensive semantic annotations: development of the Medical Entity and Relation LIMSI annOtated Text corpus (MERLOT)

Campillos

Deléger

Grouin

et al. 2017

Lang Resources & Evaluation

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Quality annotated resources are essential for Natural Language Processing. The objective of this work is to present a corpus of clinical narratives in French annotated for linguistic, semantic and structural information, aimed at clinical information extraction. Six annotators contributed to the corpus annotation, using a comprehensive annotation scheme covering 21 entities, 11 attributes and 37 relations. All annotators trained on a small, common portion of the corpus before proceeding independently. An automatic tool was used to produce entity and attribute pre-annotations. About a tenth of the corpus was doubly annotated and annotation differences were resolved in consensus meetings. To ensure annotation consistency throughout the corpus, we devised harmonization tools to automatically identify annotation differences to be addressed to improve the overall corpus quality. The annotation project spanned over 24 months and resulted in a corpus comprising 500 documents (148,476 tokens) annotated with 44,740 entities and 26,478 relations. The average inter-annotator agreement is 0.793 F-measure for entities and 0.789 for relations. The performance of the pre-annotation tool for entities reached 0.814 F-measure when sufficient training data was available. The performance of our entity pre-annotation tool shows the value of the corpus to build and evaluate information extraction methods. In addition, we introduced harmonization methods that further improved the quality of annotations in the corpus.Electronic supplementary material The online version of this article

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Section: Introductionmentioning

confidence: 99%

A French clinical corpus with comprehensive semantic annotations: development of the Medical Entity and Relation LIMSI annOtated Text corpus (MERLOT)

Campillos

Deléger

Grouin

et al. 2017

Lang Resources & Evaluation

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“…The type system based on Intermountain Healthcare’s Clinical Element Models (CEMs) has been implemented in UIMA and is fully functional in cTAKES versions 2.0 and later [40]. Dictionaries such as UMLS, SNOMED CT, and RxNorm are integrated into the cTAKES clinical pipeline.…”

Section: Methodsmentioning

confidence: 99%

A computational framework for converting textual clinical diagnostic criteria into the quality data model

Hong

et al. 2016

Journal of Biomedical Informatics

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Background Constructing standard and computable clinical diagnostic criteria is an important but challenging research field in the clinical informatics community. The Quality Data Model (QDM) is emerging as a promising information model for standardizing clinical diagnostic criteria. Objective To develop and evaluate automated methods for converting textual clinical diagnostic criteria in a structured format using QDM. Methods We used a clinical Natural Language Processing (NLP) tool known as cTAKES to detect sentences and annotate events in diagnostic criteria. We developed a rule-based approach for assigning the QDM datatype(s) to an individual criterion, whereas we invoked a machine learning algorithm based on the Conditional Random Fields (CRFs) for annotating attributes belonging to each particular QDM datatype. We manually developed an annotated corpus as the gold standard and used standard measures (precision, recall and f-measure) for the performance evaluation. Results We harvested 267 individual criteria with the datatypes of Symptom and Laboratory Test from 63 textual diagnostic criteria. We manually annotated attributes and values in 142 individual Laboratory Test criteria. The average performance of our rule-based approach was 0.84 of precision, 0.86 of recall, and 0.85 of f-measure; the performance of CRFs-based classification was 0.95 of precision, 0.88 of recall and 0.91 of f-measure. We also implemented a web-based tool that automatically translates textual Laboratory Test criteria into the QDM XML template format. The results indicated that our approaches leveraging cTAKES and CRFs are effective in facilitating diagnostic criteria annotation and classification. Conclusion Our NLP-based computational framework is a feasible and useful solution in developing diagnostic criteria representation and computerization.

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“…These mentions represent basic concepts such as diseases/disorders, receptors/biomarkers, TNM stage, overall stage, tumor size, procedures, and medications. Mentions are extracted by the mention-annotation pipeline, an extension of the Apache cTAKES © natural language processing system (4) (5) (6). Descriptors such as body location, test method, and associated neoplasms are also represented at this level.…”

Section: Deepphe Systemmentioning

confidence: 99%

DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records

et al. 2017

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Precise phenotype information is needed to understand the effects of genetic and epigenetic changes on tumor behavior and responsiveness. Extraction and representation of cancer phenotypes is currently mostly performed manually making it difficult to correlate phenotypic data to genomic data. In addition, genomic data is being produced at an increasingly faster pace, exacerbating the problem. The DeepPhe software enables automated extraction of detailed phenotype information from Electronic Medical Records of cancer patients. The system implements advanced Natural Language Processing and knowledge engineering methods within a flexible modular architecture, and was evaluated using a manually-annotated dataset of the University of Pittsburgh Medical Center (UPMC) breast cancer patients. The resulting platform provides critical and missing computational methods for computational phenotyping. Working in tandem with advanced analysis of high-throughput sequencing, these approaches will further accelerate the transition to precision cancer treatment.

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A common type system for clinical natural language processing

Cited by 33 publications

References 14 publications

A French clinical corpus with comprehensive semantic annotations: development of the Medical Entity and Relation LIMSI annOtated Text corpus (MERLOT)

A French clinical corpus with comprehensive semantic annotations: development of the Medical Entity and Relation LIMSI annOtated Text corpus (MERLOT)

A computational framework for converting textual clinical diagnostic criteria into the quality data model

DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records

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