Domain Model for Medical Information Extraction—The LightMedOnt Ontology

Mykowiecka, Agnieszka; Marciniak, Małgorzata

doi:10.1007/978-3-642-04735-0_14

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…This paper presents software modules that support the automatic extraction of diagnosis. The paper describes relationships between OWL ontologies and the domain model for medical information extraction system on the medical reports written in Polish language [15]. Balabaeva et al described an approach to develop a spell checker module for clinical text in Russian [16].…”

Section: Related Workmentioning

confidence: 99%

Creating Resources for Marking Diagnoses in Electronic Health Reports in Serbian

Marovac

Avdić

Janković

et al. 2020

IJEEC

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Thanks to medical information systems, many medical reports are collected in an electronic form daily. Apart from the fields with allowed values for input (the structural part), one part of these reports consists of the free, non-structural text. It contains a more detailed description of the patient's condition, which could not be described using the structural part. Symptoms, results of laboratory analyses, accompanying diagnoses, etc. can often be found in it. Due to a lack of time, doctors often write these descriptions in non-standard ways, using their abbreviations and synonyms, and they often contain typos. All this makes it difficult to extract information in documents specific to the medical domain. This paper presents the creation of medical lexical resources for the automatic labeling of terms from diagnoses in medical reports. In order to perform the automatic marking of the free text, methods of the computer processing of natural languages are needed, as well as appropriate lexical resources. As there are no publicly available medical lexical resources for the Serbian language, as well as a corpus with medical reports, the contribution of this paper is the construction of such resources for needs of automatic marking of diagnoses. Using the proposed resources, diagnosis codes, Latin and Serbian terms specific to certain ICD-10 can be mapped with precision of 83.47%, 86.86% and 78.29%, respectively.

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Section: Related Workmentioning

confidence: 99%

Creating Resources for Marking Diagnoses in Electronic Health Reports in Serbian

Marovac

Avdić

Janković

et al. 2020

IJEEC

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Automatic Semantic Labeling of Medical Texts with Feature Structures

Mykowiecka

Marciniak

2011

Text, Speech and Dialogue

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Rule-based information extraction from patients’ clinical data

Mykowiecka

Marciniak

Kupć

2009

Journal of Biomedical Informatics

105

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The paper describes a rule-based information extraction (IE) system developed for Polish medical texts. We present two applications designed to select data from medical documentation in Polish: mammography reports and hospital records of diabetic patients. First, we have designed a special ontology that subsequently had its concepts translated into two separate models, represented as typed feature structure (TFS) hierarchies, complying with the format required by the IE platform we adopted. Then, we used dedicated IE grammars to process documents and fill in templates provided by the models. In particular, in the grammars, we addressed such linguistic issues as: ambiguous keywords, negation, coordination or anaphoric expressions. Resolving some of these problems has been deferred to a post-processing phase where the extracted information is further grouped and structured into more complex templates. To this end, we defined special heuristic algorithms on the basis of sample data. The evaluation of the implemented procedures shows their usability for clinical data extraction tasks. For most of the evaluated templates, precision and recall well above 80% were obtained.

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Domain Model for Medical Information Extraction—The LightMedOnt Ontology

Cited by 3 publications

References 8 publications

Creating Resources for Marking Diagnoses in Electronic Health Reports in Serbian

Creating Resources for Marking Diagnoses in Electronic Health Reports in Serbian

Automatic Semantic Labeling of Medical Texts with Feature Structures

Rule-based information extraction from patients’ clinical data

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