Semantic Text Parsing for Patient Records

Friedman, Carol

doi:10.1007/0-387-25739-x_15

Cited by 11 publications

(7 citation statements)

References 21 publications

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“…MedLEE has been successfully used to process radiology reports, discharge summaries, sign-out notes, pathology reports, electrocardiogram reports, and echocardiogram reports [10, 67, 68, 69, 70]. An in-depth overview of the system and a case scenario are provided in [71]. …”

Section: Current State Of Clinical Nlp Systemsmentioning

confidence: 99%

What can natural language processing do for clinical decision support?

Demner‐Fushman

Chapman

McDonald

2009

Journal of Biomedical Informatics

535

305

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Computerized Clinical Decision Support (CDS) aims to aid decision making of health care providers and the public by providing easily accessible health-related information at the point and time it is needed. Natural Language Processing (NLP) is instrumental in using free-text information to drive CDS, representing clinical knowledge and CDS interventions in standardized formats, and leveraging clinical narrative. The early innovative NLP research of clinical narrative was followed by a period of stable research conducted at the major clinical centers and a shift of mainstream interest to biomedical NLP. This review primarily focuses on the recently renewed interest in development of fundamental NLP methods and advances in the NLP systems for CDS. The current solutions to challenges posed by distinct sublanguages, intended user groups, and support goals are discussed.

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Section: Current State Of Clinical Nlp Systemsmentioning

confidence: 99%

What can natural language processing do for clinical decision support?

Demner‐Fushman

Chapman

McDonald

2009

Journal of Biomedical Informatics

535

305

View full text Add to dashboard Cite

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“…Tasks or modules in the framework can be dependent or independent and are organized sequentially or hierarchically. 5 Background knowledge for NLP in biomedicine: The Unified Medical Language System (UMLS)…”

Section: Figmentioning

confidence: 99%

“…These tools can help biomedical researchers re-use and adapt NLP tools efficiently in biomedicine. Several software frameworks that facilitate the integration of different tools into a single pipeline have been developed, such as GATE 5 (General Architecture for Text Engineering) and UIMA 6 (Unstructured Information Management Architecture). Given the success of IBM's Watson in the 2011 Jeopardy challenge, the UIMA framework, which was used for real-time content analysis in Watson, has now been applied widely by the biomedical NLP community.…”

Section: Introductionmentioning

confidence: 99%

“…Spyns provided an overview of pre-1996 biomedical NLP systems [3], while Demner-Fushman et al more recently reviewed and summarized NLP methods and systems for clinical decision support [4]. The use of NLP in medicine has been comprehensively reviewed by Friedman [5], Nadkami et al [6], and more recently by Friedman and Elhadad [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.…”

Section: Introductionmentioning

confidence: 99%

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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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“…The body site of interest is the one of the primary tumor. MedLEE 17 18 ‘has an integrated syntactic and semantic component which is realized in the form of its grammar. The MedLEE grammar consists of a specification of semantic (and sometimes syntactic) components and is used to interpret the semantic properties of the individual terms and of their relations with other terms, and to generate a target output.…”

Section: Background and Significancementioning

confidence: 99%

Discovering body site and severity modifiers in clinical texts

Dligach

Bethard

Becker

et al. 2014

J Am Med Inform Assoc

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ObjectiveTo research computational methods for discovering body site and severity modifiers in clinical texts.MethodsWe cast the task of discovering body site and severity modifiers as a relation extraction problem in the context of a supervised machine learning framework. We utilize rich linguistic features to represent the pairs of relation arguments and delegate the decision about the nature of the relationship between them to a support vector machine model. We evaluate our models using two corpora that annotate body site and severity modifiers. We also compare the model performance to a number of rule-based baselines. We conduct cross-domain portability experiments. In addition, we carry out feature ablation experiments to determine the contribution of various feature groups. Finally, we perform error analysis and report the sources of errors.ResultsThe performance of our method for discovering body site modifiers achieves F1 of 0.740–0.908 and our method for discovering severity modifiers achieves F1 of 0.905–0.929.DiscussionResults indicate that both methods perform well on both in-domain and out-domain data, approaching the performance of human annotators. The most salient features are token and named entity features, although syntactic dependency features also contribute to the overall performance. The dominant sources of errors are infrequent patterns in the data and inability of the system to discern deeper semantic structures.ConclusionsWe investigated computational methods for discovering body site and severity modifiers in clinical texts. Our best system is released open source as part of the clinical Text Analysis and Knowledge Extraction System (cTAKES).

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Semantic Text Parsing for Patient Records

Cited by 11 publications

References 21 publications

What can natural language processing do for clinical decision support?

What can natural language processing do for clinical decision support?

Natural Language Processing in Biomedicine: A Unified System Architecture Overview

Discovering body site and severity modifiers in clinical texts

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