Textractor: a hybrid system for medications and reason for their prescription extraction from clinical text documents

Meystre, Stéphane M.; Thibault, Julien; Shen, Shuying; Hurdle, John F.; South, Brett R.

doi:10.1136/jamia.2010.004028

Cited by 42 publications

(26 citation statements)

References 12 publications

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“…They then used regular expressions to identify medications from a manually curated list and to identify applicable lab test results. They used the UMLS Metathesaurus module in Textractor (Meystre et al, 2010) to identify diseases and risk factors, then match them to the Concept Unique Identifiers (CUIs) that apply to the RF track. Finally, they performed a contextual analysis to remove risk factors that do not relate to the patient (i.e., negated), and identify family history of CAD using ConText.…”

Section: Submissionsmentioning

confidence: 99%

Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2

Stubbs

Kotfila

et al. 2015

Journal of Biomedical Informatics

107

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The second track of the 2014 i2b2/UTHealth Natural Language Processing shared task focused on identifying medical risk factors related to Coronary Artery Disease (CAD) in the narratives of longitudinal medical records of diabetic patients. The risk factors included hypertension, hyperlipidemia, obesity, smoking status, and family history, as well as diabetes and CAD, and indicators that suggest the presence of those diseases. In addition to identifying the risk factors, this track of the 2014 i2b2/UTHealth shared task studied the presence and progression of the risk factors in longitudinal medical records. Twenty teams participated in this track, and submitted 49 system runs for evaluation. Six of the top 10 teams achieved F1 scores over 0.90, and all 10 scored over 0.87. The most successful system used a combination of additional annotations, external lexicons, hand-written rules and Support Vector Machines. The results of this track indicate that identification of risk factors and their progression over time is well within the reach of automated systems.

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Section: Submissionsmentioning

confidence: 99%

Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2

Stubbs

Kotfila

et al. 2015

Journal of Biomedical Informatics

107

View full text Add to dashboard Cite

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“…Hamon and Grabar [13] created a lexicon of medication names using RxNorm and created regular expressions to identify associated information. Meystre et al developed a system called Textractor [14] that uses cTAKES for routine NLP tasks such as tokenization, chunking, and segmentation. Further, they used Metamap to identify drug names and their reasons and regular expressions for other fields of interest.…”

Section: Related Workmentioning

confidence: 99%

Comparison of UMLS terminologies to identify risk of heart disease using clinical notes

Shivade

Malewadkar

Fosler‐Lussier

et al. 2015

Journal of Biomedical Informatics

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The second track of the 2014 i2b2 challenge asked participants to automatically identify risk factors for heart disease among diabetic patients using natural language processing techniques for clinical notes. This paper describes a rule-based system developed using a combination of regular expressions, concepts from the Unified Medical Language System (UMLS), and freely-available resources from the community. With a performance (F1=90.7) that is significantly higher than the median (F1=87.20) and close to the top performing system (F1=92.8), it was the best rule-based system of all the submissions in the challenge. This system was also used to evaluate the utility of different terminologies in the UMLS towards the challenge task. Of the 155 terminologies in the UMLS, 129 (76.78%) have no representation in the corpus. The Consumer Health Vocabulary had very good coverage of relevant concepts and was the most useful terminology for the challenge task. While segmenting notes into sections and lists has a significant impact on the performance, identifying negations and experiencer of the medical event results in negligible gain.

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“…[34] used it to extract cause of death from electronic health records, while Meystre et al [35] used it to extract medication information from the clinical record. Pakhomov et al [36] used MetaMap to extract Health Related Quality of Life Indicators from diabetes patients described in physician notes.…”

Section: Pipelinementioning

confidence: 99%

“…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]. Extraction) is an open-source NLP system (under i2b2 software license) developed at Brigham and Women's Hospital and Harvard Medical School.…”

Section: Figmentioning

confidence: 99%

See 1 more Smart Citation

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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Textractor: a hybrid system for medications and reason for their prescription extraction from clinical text documents

Abstract: The official evaluation of Textractor for the i2b2 medication extraction challenge demonstrated satisfactory performance. This system was among the 10 best performing systems in this challenge.

Cited by 42 publications

References 12 publications

Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2

Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2

Comparison of UMLS terminologies to identify risk of heart disease using clinical notes

Natural Language Processing in Biomedicine: A Unified System Architecture Overview

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