Rule-based information extraction from patients’ clinical data

Mykowiecka, Agnieszka; Marciniak, Małgorzata; Kupć, Anna

doi:10.1016/j.jbi.2009.07.007

Cited by 109 publications

(64 citation statements)

References 21 publications

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“…Recently, several rule based information extraction systems were developed. Among them we cite: TextMarker [3], the AVATAR Information Extraction System [4] and, different systems for the medical [5] and biological (gene analysis) domains [6]. Furthermore, in the last decade, a number of ontology development related research directions emerged as: the automation of ontology development (KYOTO Project [7]), ontology and lexicon integration [8,9], or ontology learning and population [1,10].…”

Section: Related Workmentioning

confidence: 99%

An Architecture for Data and Knowledge Acquisition for the Semantic Web: The AGROVOC Use Case

Pazienza

Stellato

Tudorache

et al. 2012

On the Move to Meaningful Internet Systems: OTM 2012 Workshops

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Abstract. We are surrounded by ever growing volumes of unstructured and weakly-structured information, and for a human being, domain expert or not, it is nearly impossible to read, understand and categorize such information in a fair amount of time. Moreover, different user categories have different expectations: final users need easy-to-use tools and services for specific tasks, knowledge engineers require robust tools for knowledge acquisition, knowledge categorization and semantic resources development, while semantic applications developers demand for flexible frameworks for fast and easy, standardized development of complex applications. This work represents an experience report on the use of the CODA framework for rapid prototyping and deployment of knowledge acquisition systems for RDF. The system integrates independent NLP tools and custom libraries complying with UIMA standards. For our experiment a document set has been processed to populate the AGROVOC thesaurus with two new relationships.

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Section: Related Workmentioning

confidence: 99%

An Architecture for Data and Knowledge Acquisition for the Semantic Web: The AGROVOC Use Case

Pazienza

Stellato

Tudorache

et al. 2012

On the Move to Meaningful Internet Systems: OTM 2012 Workshops

View full text Add to dashboard Cite

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“…NLP algorithms can extract meaningful information from free text and have been successfully applied to radiology reports to identify positive findings, recommendations, and tumor status [6][7][8][9]. Specific to breast imaging, NLP has been applied to mammography reports to identify findings suspicious for breast cancer [10], correlate findings and their locations [11], determine BI-RADS breast tissue composition [12], and extract multiple other reported attributes [13]. We hypothesized that NLP can accurately extract BI-RADS final assessment categories from radiology reports.…”

Section: Introductionmentioning

confidence: 99%

Automated Extraction of BI-RADS Final Assessment Categories from Radiology Reports with Natural Language Processing

et al. 2013

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The objective of this study is to evaluate a natural language processing (NLP) algorithm that determines American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) final assessment categories from radiology reports. This HIPAA-compliant study was granted institutional review board approval with waiver of informed consent. This cross-sectional study involved 1,165 breast imaging reports in the electronic medical record (EMR) from a tertiary care academic breast imaging center from 2009. Reports included screening mammography, diagnostic mammography, breast ultrasound, combined diagnostic mammography and breast ultrasound, and breast magnetic resonance imaging studies. Over 220 reports were included from each study type. The recall (sensitivity) and precision (positive predictive value) of a NLP algorithm to collect BI-RADS final assessment categories stated in the report final text was evaluated against a manual human review standard reference. For all breast imaging reports, the NLP algorithm demonstrated a recall of 100.0 % (95 % confidence interval (CI), 99.7, 100.0 %) and a precision of 96.6 % (95 % CI, 95.4, 97.5 %) for correct identification of BI-RADS final assessment categories. The NLP algorithm demonstrated high recall and precision for extraction of BI-RADS final assessment categories from the free text of breast imaging reports. NLP may provide an accurate, scalable data extraction mechanism from reports within EMRs to create databases to track breast imaging performance measures and facilitate optimal breast cancer population management strategies.

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“…Rule-based approaches have been widely developed to extract entities in the clinical domain [84,85]. They are based on regular expressions, heuristics and rules.…”

Section: Rules-based Approachmentioning

confidence: 99%

“…Additionally, these approaches are not adaptable and transferable to other domains or other languages [15]. There are examples of rule-based systems for analysing clinical records and extracting clinical entities for different languages such as Polish [84], Dutch [92], and Swedish [93], which shows that existing systems that have been developed based on English clinical information systems are not directly adaptable to other languages.…”

Section: Rules-based Approachmentioning

confidence: 99%

Active Learning for Concept Extraction from Clinical Free Text

Kholghi¹

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An increasing volume of clinical free-text data, such as discharge summaries and progress reports, has been collected by hospitals and healthcare centres and stored electronically for further processing. Extracting structured clinical information from such unstructured text resources is necessary for enabling secondary usage of reports, such as reporting, reasoning and retrieving, and for further processing in down-stream eHealth workflows. However, this analysis cannot be done manually, due to the high cost incurred by qualified experts to annotate the clinical free text. A significant initial step in extracting information from clinical free text is concept extraction, which involves identifying entities of interest in the clinical domain (such as diseases, medications, and symptoms). Currently, supervised machine learning approaches effectively extract clinical concepts by building powerful statistical models. However, these approaches require a large amount of high quality, annotated train data, which is created manually by domain experts through a costly and timeconsuming process. This results in a robust active learning framework for extracting clinical concepts, using state-of-the-art, active learning approaches. The second step is to leverage clinical information resources (i.e., terminologies and clinical information extraction tools) and other machine learning approaches (i.e., semi-supervised learning, unsupervised learning, and representation learning) to develop domain-specific and generic active learning approaches. This leads to a number of novel, active learning query strategies and a seed selection approach that outperform the state-of-the-art approaches with less manual annotation effort. The last step is to validate the benefits of the developed AL-based framework in reducing the annotation cost (i.e., time) through a comprehensive user study. An AL-assisted pre-annotation scheme is also introduced, in which the learning models built across the AL process generate high quality pre-annotations to be reviewed by human annotators. This further accelerates the annotation process, by significantly reducing the number of manual annotations that must be added or corrected compared to de novo annotation.The results of this study demonstrate that AL plays an important role in reducing the manual annotation cost. The CEAL framework extracts high quality domain concepts from clinical narratives, while significantly reducing the labour cost with up to 35% less annotation time required. Additionally, AL-assisted preannotations accelerate the de novo annotation process with a further 20% less annotation time required. This thesis contributes to information extraction from clinical unstructured text resources by alleviating the burden of manual annotation.The practical significance of this research is three-fold: (1) benefitting the overall patient healthcare by facilitating downstream eHealth workflows such as supporting clinical information processing, reporting, reasoning, and efficient decision m...

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Rule-based information extraction from patients’ clinical data

Cited by 109 publications

References 21 publications

An Architecture for Data and Knowledge Acquisition for the Semantic Web: The AGROVOC Use Case

An Architecture for Data and Knowledge Acquisition for the Semantic Web: The AGROVOC Use Case

Automated Extraction of BI-RADS Final Assessment Categories from Radiology Reports with Natural Language Processing

Active Learning for Concept Extraction from Clinical Free Text

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