Identifying Condition-Action Sentences Using a Heuristic-Based Information Extraction Method

Wenzina, Reinhardt; Kaiser, Katharina

doi:10.1007/978-3-319-03916-9_3

Cited by 18 publications

(20 citation statements)

References 15 publications

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“…Zadrozny et al (2017) outline a system which identifies contradictions and disagreements in English CPGs. Some authors have focused on extracting more task-specific information, such as activities (Kaiser et al, 2010), process structures (Wenzina and Kaiser, 2013;Zhu et al, 2013; or negation triggers (Gindl et al, 2008). Taboada et al (2013) apply a pipeline of open-source tools for parsing CPGs, Named Entity Recognition (NER) tagging and relation extraction in a case study with 171 sentences from CPGs.…”

Section: Related Workmentioning

confidence: 99%

GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines

Borchert¹,

Lohr²,

Modersohn³

et al. 2020

Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis

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The lack of publicly accessible text corpora is a major obstacle for progress in natural language processing. For medical applications, unfortunately, all language communities other than English are low-resourced. In this work, we present GGPONC (German Guideline Program in Oncology NLP Corpus), a freely distributable German language corpus based on clinical practice guidelines for oncology. This corpus is one of the largest ever built from German medical documents. Unlike clinical documents, clinical guidelines do not contain any patient-related information and can therefore be used without data protection restrictions. Moreover, GGPONC is the first corpus for the German language covering diverse conditions in a large medical subfield and provides a variety of metadata, such as literature references and evidence levels. By applying and evaluating existing medical information extraction pipelines for German text, we are able to draw comparisons for the use of medical language to other corpora, medical and non-medical ones.

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Section: Related Workmentioning

confidence: 99%

GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines

Borchert¹,

Lohr²,

Modersohn³

et al. 2020

Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis

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“…Firstly, rules were used to identify potential contradictions in the personalization of clinical processes due to patient's preferences or comorbidities [ [53.59]], or to represent and manage medical exceptions that may occur during the enactment of a patient-centered care pathway [66]. Another application of rules was to help automate the process of modelling a clinical guideline [68,25]. In [74], authors showed how rules can be used to detect the level of evidence in medical guidelines.…”

Section: Decision Tables and Rulesmentioning

confidence: 99%

“…The three dominant medical informatics foci on which the KR4HC community has been developing special representations are clinical guidelines or clinical pathways (computer-interpretable clinical guidelines (CIG 4 ) [102], electronic health/patient records, and medical domain ontologies). In particular, the topic of representing and reasoning with clinical guidelines is intensively studied (e.g., [37,46,54,61,68,73,83,87]). In some years, for instance 2009, 2014, 2015 and 2017, more than half of the papers had to do with clinical guidelines.…”

Section: Medical Informatics-specific Knowledge Representationsmentioning

confidence: 99%

“…The most highly used medical vocabularies are UMLS [33,41,47,53,62,66,68,25], SNOMED-CT [34,22,62,72,74,93], MeSH [100], and the International Classification of Primary Care (ICPC) [34]. These vocabularies are used for mapping protocol elements [32,93] or computer interpretable guideline elements [19,46,24] to patient health records.…”

Section: Medical Informatics-specific Knowledge Representationsmentioning

confidence: 99%

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Ten years of knowledge representation for health care (2009–2018): Topics, trends, and challenges

Riaño

Peleg

Teije

2019

Artificial Intelligence in Medicine

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Background: In the last ten years, the international workshop on knowledge representation for health care (KR4HC) has hosted outstanding contributions of the artificial intelligence in medicine community pertaining to the formalization and representation of medical knowledge for supporting clinical care. Contributions regarding modeling languages, technologies and methodologies to produce these models, their incorporation into medical decision support systems, and practical applications in concrete medical settings have been the main contributions and the basis to define the evolution of this field across Europe and worldwide. Objectives: Carry out a review of the papers accepted in KR4HC in the 2009-2018 decade, analyze and characterize the topics and trends within this field, and identify challenges for the evolution of the area in the near future. Methods: We reviewed the title, the abstract, and the keywords of the 112 papers that were accepted to the workshop, identified the medical and technological topics involved in these works, provided a classification of these papers in medical and technological perspectives and obtained the timeline of these topics in order to determine interest growths and declines. The experience of the authors in the field and the evidences after the review were the basis to propose a list of challenges of knowledge representation in health care for the future. Results: The most generic knowledge representation methods are ontologies (31%), semantic web related formalisms (26%), decision tables and rules (19%), logic (14%), and probabilistic models (10%). From a medical informatics perspective, knowledge is mainly represented as computer interpretable clinical guidelines (43%), medical domain ontologies (26%), and electronic health care records (22%). Within the knowledge lifecycle, contributions are found in knowledge generation (38%), knowledge specification (24%), exception detection and management (12%), knowledge enactment (8%), temporal knowledge and reasoning (7%), and knowledge sharing and maintenance (7%). The clinical emphasis of knowledge is mainly related to clinical treatments (27%), diagnosis (13%), clinical quality indicators (13%), and guideline integration for multimorbid patients (12%). According to the level of development of the works presented, we distinguished four maturity levels: formal (22%), implementation (52%), testing (13%), and deployment (2%) levels. Some papers described technologies for specific clinical issues or diseases, mainly cancer (22%) and diseases of the circulatory system (20%). Chronicity and comorbidity were present in 10% and 8% of the papers, respectively. Conclusions: KR4HC is a stable community, still active after ten years. A persistent focus has been knowledge representation, with an emphasis on semantic-web ontologies and on clinical-guideline based decision-support. Among others, two topics receive growing attention: integration of computer-interpretable guideline knowledge for the management of multimorbidity patients, and patie...

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“…[7][8][9][10][11][12][13][14] These approaches are either based on a set of manual steps to gradually convert narrative text CPGs into CIGs, 7,8 or based on automated information extraction mechanisms frequently using linguistic patterns. [9][10][11][12][13][14] While the accuracy of the manual approaches is straightforwardly controlled, as the resulted accuracy is as good as the input provided by the human modellers, these approaches are impractical to use in formalizing large numbers of CPGs. On the other hand, the automated and semi-automated information extraction-based approaches are more suited to formalize a relative large number of CPGs, but these approaches have not shown how expandable they are in handling a large number of heterogeneous CPGs, CPGs with different styles, granularity, and so on -a common challenge with all large-scale information extraction systems.…”

Section: Introductionmentioning

confidence: 99%

Formalizing clinical practice guideline for clinical decision support systems

El-Rab

Zaı̈ane

El-Hajj

2016

Health Informatics J

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Clinical practice guidelines are valuable sources of clinical knowledge for healthcare professionals. However, the passive dissemination of clinical practice guidelines like publishing in medical journals is ineffective in changing clinical practice behaviour. In this work, we proposed a framework to help adopting an active clinical practice guideline dissemination approach by automatically extracting clinical knowledge from clinical practice guidelines into a clinical decision support system-friendly format. The proposed framework is intended to help human modellers by automating some of the manual formalization activities in order to minimize their manual effort. We evaluated our framework using all recommendations from two clinical practice guidelines produced by the Scottish Intercollegiate Guidelines Network: the 'Management of lung cancer' clinical practice guideline and the 'Management of chronic pain' clinical practice guideline. We conclude that the proposed framework can be effectively used to formalize drug and procedure recommendation in clinical contexts.

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Identifying Condition-Action Sentences Using a Heuristic-Based Information Extraction Method

Cited by 18 publications

References 15 publications

GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines

GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines

Ten years of knowledge representation for health care (2009–2018): Topics, trends, and challenges

Formalizing clinical practice guideline for clinical decision support systems

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