Comparison of a semi-automatic annotation tool and a natural language processing application for the generation of clinical statement entries

Lin, Ching Heng; Wu, Nai Yuan; Lai, Wei Shao; Liou, Der Ming

doi:10.1136/amiajnl-2014-002991

Cited by 9 publications

(7 citation statements)

References 27 publications

(27 reference statements)

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“…In 2014, Lin et al combined NLP with a semi-automatic annotation approach to generate entry-level CDA documents. 13 Before, in 2012, Meystre et al combined HL7 CDA with the ISO Graph Annotation Format to develop a new standard-based data model out of unstructured clinical data, tested on discharge summaries and progress notes. 14 As already denoted in the introduction, for openEHR, we only identified one other article in this context, published by Kropf et al 15 Their work from 2017 shows initial successful attempts to use openEHR archetypes as final structured representation of a German pathology report.…”

Section: Discussionmentioning

confidence: 99%

“… 12 Some older publications dealing with HL7 CDA for structuring texts such as discharge letters are available, too. 13 14 In terms of openEHR, Kropf et al 15 presented a way to structure a pathology report into sections represented by openEHR archetypes by a regular expression-based approach to enable section-sensitive queries on these texts. The work successfully shows the feasibility of transforming the general structure of a document into an openEHR-based representation and formulating semantic queries on previously unstructured pathology reports.…”

Section: Introductionmentioning

confidence: 99%

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Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing

Wulff

Mast

Hassler³

et al. 2020

Methods Inf Med

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Background Merging disparate and heterogeneous datasets from clinical routine in a standardized and semantically enriched format to enable a multiple use of data also means incorporating unstructured data such as medical free texts. Although the extraction of structured data from texts, known as natural language processing (NLP), has been researched at least for the English language extensively, it is not enough to get a structured output in any format. NLP techniques need to be used together with clinical information standards such as openEHR to be able to reuse and exchange still unstructured data sensibly. Objectives The aim of the study is to automatically extract crucial information from medical free texts and to transform this unstructured clinical data into a standardized and structured representation by designing and implementing an exemplary pipeline for the processing of pediatric medical histories. Methods We constructed a pipeline that allows reusing medical free texts such as pediatric medical histories in a structured and standardized way by (1) selecting and modeling appropriate openEHR archetypes as standard clinical information models, (2) defining a German dictionary with crucial text markers serving as expert knowledge base for a NLP pipeline, and (3) creating mapping rules between the NLP output and the archetypes. The approach was evaluated in a first pilot study by using 50 manually annotated medical histories from the pediatric intensive care unit of the Hannover Medical School. Results We successfully reused 24 existing international archetypes to represent the most crucial elements of unstructured pediatric medical histories in a standardized form. The self-developed NLP pipeline was constructed by defining 3.055 text marker entries, 132 text events, 66 regular expressions, and a text corpus consisting of 776 entries for automatic correction of spelling mistakes. A total of 123 mapping rules were implemented to transform the extracted snippets to an openEHR-based representation to be able to store them together with other structured data in an existing openEHR-based data repository. In the first evaluation, the NLP pipeline yielded 97% precision and 94% recall. Conclusion The use of NLP and openEHR archetypes was demonstrated as a viable approach for extracting and representing important information from pediatric medical histories in a structured and semantically enriched format. We designed a promising approach with potential to be generalized, and implemented a prototype that is extensible and reusable for other use cases concerning German medical free texts. In a long term, this will harness unstructured clinical data for further research purposes such as the design of clinical decision support systems. Together with structured data already integrated in openEHR-based representations, we aim at developing an interoperable openEHR-based application that is capable of automatically assessing a patient's risk status based on the patient's medical history at time of admission.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing

Wulff

Mast

Hassler³

et al. 2020

Methods Inf Med

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“…SNOMED annotations were performed on natural language text with NLP and they were found to improve the semantic interoperability of documents. [27,28,29]…”

Section: Introductionmentioning

confidence: 99%

SNOMED CT Annotation for Improved Pathological Decisions in Breast Cancer Domain

Rani*¹,

Gladi²,

Mammen³

2019

IJRTE

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Breast cancer pathology reports are used in the diagnosis of the disease and determination of the stage of cancer in a patient. These reports are written or electronically generated by the Pathologist in English. The contents of a Pathology report generated by the Pathologist are usually in unstructured natural language form. The contents of a report are used to determine the Pathological classification and Cancer stage of a patient. Information extraction and making pathological decisions from natural language text is a complex process due to the heterogeneity of the report structure and its contents. The reports can be homogenized using the global annotation standard Systematized Nomenclature of Medicine – Clinical Terms, SNOMED-CT. It enables consistent representations of medical terms and can be used for clinical decision support systems (CDSS) and cancer reporting. SNOMED is a vast repository and its enormity and complexity necessitates extraction of a subset for a particular domain before using it for annotation. The annotation is performed either in online mode at the time of generation of the report or in offline mode on a batch of archived reports. A CDSS prototype is developed for breast cancer domain, which provides support to the Pathologist to determine the Pathological Classification and Cancer Staging on both natural language text and SNOMED-annotated text. With regard to Pathological decisions, a hypothesis is formulated that Annotation using SNOMED does not improve the system’s performance in determining the cancer stage of a patient. For annotating the text, the system initially extracts a SNOMED subset for the domain. Performance Analysis of the decision support processes was done by determining Precision, Recall, Specificity, Accuracy, F-measure and Error. The analysis indicates that the annotation feature improved the accuracy of automated Pathological decisions presented by the CDSS to the Clinician for finalizing his decisions. In the future, the CDSS feature can be applied to other cancer domains and thus provide a means to improve decision-making related to those domains.

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“…Typical clinical NLP tools that could support term recognition and text annotation from clinical text include Health Information Text Extraction tool (HITex) [7], MetaMap [8], OpenNLP [9], and cTAKES [10]. Some studies compared the performance of these frequently used NLP tools, and the cTAKES shows satisfactory performance and usability [11, 12]. cTAKES is an open-source Apache project and is an NLP system designed to extract information from EHR-based clinical free-text.…”

Section: Introductionmentioning

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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Comparison of a semi-automatic annotation tool and a natural language processing application for the generation of clinical statement entries

Abstract: The combination of a semi-automatic annotation approach and the NLP application seems to be a solution for generating entry-level interoperable clinical documents.

Cited by 9 publications

References 27 publications

Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing

Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing

SNOMED CT Annotation for Improved Pathological Decisions in Breast Cancer Domain

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

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