Extracting Rx information from clinical narrative

Mork, James G.; Bodenreider, Olivier; Demner‐Fushman, Dina; Doğan, Rezarta Islamaj; Lang, François-Michel; Lu, Zhiyong; Névéol, Aurélie; Peters, Lee B.; Shooshan, Sonya E.; Aronson, Alan R.

doi:10.1136/jamia.2010.003970

Cited by 28 publications

(26 citation statements)

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“…We included 9 of the 15 UMLS SGs: Anatomy, Chemicals and Drugs, Concepts and Ideas, Devices, Disorders, Genes and Molecular Sequences, Living Beings, Physiology and Procedures. Note that the semantic type (hereafter, STY) Findings was not included in the Disorder class, because prior work has shown this category to yield many false positives (Mork et al 2010;Névéol et al 2009). We also created four additional categories for annotating elements of clinical interest: -SignOrSymptom: Signs/Symptoms and Disorders are separate categories.…”

Section: Annotation Schemementioning

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: Annotation Schemementioning

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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“…We plan to apply both methods to include additional relationships into the repository. In addition, we intend to explore another method of retrieving drug/disease relationships from UMLS vocabularies such as NDF-RT [12].…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, a comprehensive source of drug/disease relationships was identified as missing from public repositories in the recent i2b2 challenge [12]. These studies indicate that there is a strong need for a comprehensive resource of drug/disease relationships.…”

Section: Need For Specific Drug/disease Informationmentioning

confidence: 99%

“…• Action on the symptoms of a DISEASE The analysis of text describing a drug's action on specific symptoms of a disease also proved difficult. For example, if the drug treats all symptoms of a specific disease (12), it would be reasonable to infer that the drug treats the disease (R12b) rather than the symptoms (R12a). However, if a drug treats a particular symptom of a disease (13) it should be inferred that the drug treats the specific symptom (R13b) rather than the disease in general (R13a).…”

Section: Direct Evaluation Of Relationship Samplesmentioning

confidence: 99%

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Automatic integration of drug indications from multiple health resources

Névéol

2010

Proceedings of the 1st ACM International Health Informatics Symposium

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Drug indication refers to what disease(s) a drug may treat -a type of information that is frequently sought by biomedical researchers, health care professionals and the general public. Although such information may be available online, it is often challenging for non-experts to glean unbiased reliable information from multiple websites of various quality. In addition, most drug indication information is only available in free text as opposed to structured format, thus making it difficult for further automatic analysis by computers. In response, we herein focus on automatically extracting and integrating drug indication information from multiple resources such as DailyMed and MeSH Scope notes. We select trustworthy resources of drug/disease relationships and apply state-of-the-art relationship extraction methods, customized to improve recall and perform ellipsis and anaphora resolution. As a result, 7,670 unique TREATS relationships between 4,666 drugs and 1,293 diseases are integrated from 4 different sources with an estimated overall correctness of 77% and specificity of 84%.

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“…It achieved an F-score of 0.803, with Precision 0.784 and Recall 0.823. Although it ranked fourth in the challenge, it had the highest Recall among participating teams [39,40]. Another system that used MetaMap, Textrator, developed by Meystre et al was also among the top ten in that competition [35,40].…”

Section: Figmentioning

confidence: 99%

Natural Language Processing in Biomedicine: A Unified System Architecture Overview

Doan

Conway

Phương³

et al. 2014

Methods in Molecular Biology

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In modern electronic medical records (EMR) much of the clinically important data -signs and symptoms, symptom severity, disease status, etc. -are not provided in structured data fields, but rather are encoded in clinician generated narrative text. Natural language processing (NLP) provides a means of "unlocking" this important data source for applications in clinical decision support, quality assurance, and public health. This chapter provides an overview of representative NLP systems in biomedicine based on a unified architectural view. A general architecture in an NLP system consists of two main components: background knowledge that includes biomedical knowledge resources and a framework that integrates NLP tools to process text. Systems differ in both components, which we will review briefly. Additionally, challenges facing current research efforts in biomedical NLP include the paucity of large, publicly available annotated corpora, although initiatives that facilitate data sharing, system evaluation, and collaborative work between researchers in clinical NLP are starting to emerge. IntroductionIn modern electronic medical records (EMR) most of the clinically important data -signs and symptoms, symptom severity, disease status, etc. -is not provided in structured data fields, but are rather encoded in clinician-generated narrative text. Natural language processing (NLP) provides a means of "unlocking" this important data source, converting unstructured text to structured, actionable data for use in applications for clinical decision support, quality assurance, and public health surveillance. There are currently many NLP systems that have been 2 successfully applied to biomedical text. It is not our goal to review all of them in this chapter, but rather to provide an overview of how the field evolved from producing monolithic software built on platforms that were available at the time they were developed to contemporary component-based systems built on top of general frameworks. More importantly, the performance of these systems is tightly associated with their "ingredients" (i.e., modules that are used to form its background knowledge), and how these modules are combined on top of the general framework. We highlight certain systems based on their landmark status as well as on the diversity of components and frameworks they are based on. [7]. The review in this chapter differs from previous work in that it emphasizes the historical development of landmark clinical NLP systems, and presents each system in light of a unified system architecture.We consider that each NLP system in biomedicine contains two main components: biomedical background knowledge and a framework that integrates NLP tools. In the rest of this paper, we will first outline our model architecture for NLP systems in biomedicine, before going on to review and summarize representative NLP systems, starting with an early NLP system, LSP-MLP, and closing our discussion with the presentation of a more recent system, cTAKES. Finally, we will discuss...

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Extracting Rx information from clinical narrative

Abstract: The authors plan to integrate the new modules with MetaMap to enhance its accuracy. This integration effort will provide guidance in retargeting existing tools for better processing of clinical text.

Cited by 28 publications

References 5 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)

Automatic integration of drug indications from multiple health resources

Natural Language Processing in Biomedicine: A Unified System Architecture Overview

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