Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system

Zeng, Qing; Goryachev, Sergey; Weiss, Scott T.; Sordo, Margarita; Murphy, Shawn N.; Lazarus, Ross

doi:10.1186/1472-6947-6-30

Cited by 295 publications

(221 citation statements)

References 15 publications

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“…The approach used by HITEx is close to our study but UMLS tables for mapping the concepts were used [6]. To the best of our knowledge SNOMED CT reference terminology is the most consistent and complete vocabulary covering maximum concepts and the relationship between the concepts.…”

Section: Figure 4 Example Of Inter Sentence Patternmentioning

confidence: 99%

“…Another system used lexicons for the semantic matching words and phrases [5]. Health Information Text Extraction (HITEx) tool also used the UMLS Database to extract UMLS concepts for the principal diagnosis [6]. A number of studies report other NLP systems for the information extraction such as MEDLEE [7], AMBIT: Acquiring Medical and Biological Information from Text [8], and MetaMap [9] for different evaluations in biomedical field.…”

Section: Introductionmentioning

confidence: 99%

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Semantic Tagging of Medical Narratives with Top Level Concepts from SNOMED CT Healthcare Data Standard

Hina¹,

Atwell²,

Johnson³

2012

IJICR

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Section: Figure 4 Example Of Inter Sentence Patternmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Semantic Tagging of Medical Narratives with Top Level Concepts from SNOMED CT Healthcare Data Standard

Hina¹,

Atwell²,

Johnson³

2012

IJICR

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“…The system has been used for the extraction of family history from 150 discharge summaries, with accuracies of 0.82 for principal diagnosis, 0.87 for co-morbidity, and 0.90 for smoking status extraction, when excluding cases labeled "Insufficient Data" in the gold standard [41,42]. Background knowledge cTAKES used trained corpora from Mayo clinic data and other sources, utilizing the UMLS as the main background knowledge.…”

Section: Pipelinementioning

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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“…medical dictionaries. These systems include MetaMap (Aronson and Lang, 2010), Hi-TEX (Zeng et al, 2006), KnowledgeMap (Denny et al, 2003), MedLEE (Friedman et al, 1994), SymText (Koehler, 1994) and Mplus (Christensen et al, 2002). In the past couple of years, researchers have been exploring the use of machine learning algorithms in the clinical concept detection.…”

Section: Introductionmentioning

confidence: 99%

ThinkMiners: Disorder Recognition using Conditional Random Fields and Distributional Semantics

Parikh

Pvs

Mustafi

et al. 2014

Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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In 2014, SemEval organized multiple challenges on natural language processing and information retrieval. One of the task was analysis of the clinical text. This challenge is further divided into two tasks. The task A of the challenge was to extract disorder mention spans in the clinical text and the task B was to map each of the disorder mentions to a unique Unified Medical Language System Concept Unique Identifier. We participated in the task A and developed a clinical disorder recognition system. The proposed system consists of a Conditional Random Fields based approach to recognize disorder entities. The SemEval challenge organizers manually annotated disorder entities in 298 clinical notes, of which 199 notes were used for training and 99 for development. On the test data, our system achieved the Fmeasure of 0.844 for entity recognition in relaxed and 0.689 in strict evaluation.

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Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system

Abstract: Background: The text descriptions in electronic medical records are a rich source of information. We have developed a Health Information Text Extraction (HITEx) tool and used it to extract key findings for a research study on airways disease.

Cited by 295 publications

References 15 publications

Semantic Tagging of Medical Narratives with Top Level Concepts from SNOMED CT Healthcare Data Standard

Semantic Tagging of Medical Narratives with Top Level Concepts from SNOMED CT Healthcare Data Standard

Natural Language Processing in Biomedicine: A Unified System Architecture Overview

ThinkMiners: Disorder Recognition using Conditional Random Fields and Distributional Semantics

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