A system for named entity recognition based on local grammars

Krstev, Cvetana; Obradović, Ivan; Utvić, Miloš; Vitas, Duško

doi:10.1093/logcom/exs079

Cited by 16 publications

(9 citation statements)

References 2 publications

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“…The existing system for the automatic processing of TEs of Serbian newspaper texts is applied to medical narrative texts to identify and normalize the TEs contained in this document type. This system is a part of the Serbian system for named entity recognition (Krstev et al, 2014). It is a rule-based system that relies on lexical resources and handles both absolute and relative time.…”

Section: Finite-state Methodologymentioning

confidence: 99%

Recognition and Normalization of Temporal Expressions in Serbian Medical Narratives

Jaćimović¹

2019

Infotheca

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The temporal dimension emerges as one of the essential concepts in the field of medicine, providing a basis for the proper interpretation and understanding of medically relevant information, often recorded only in unstructured texts. Automatic processing of temporal expressions involves their identification and formalization in a language understandable to computers. This paper aims to apply the existing system for automatic processing of temporal expressions in Serbian natural language texts to medical narrative texts, to evaluate the system's efficiency in recognition and normalization of temporal expressions and to determine the degree of necessary adaptation according to the characteristics and requirements of the medical domain.

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Section: Finite-state Methodologymentioning

confidence: 99%

Recognition and Normalization of Temporal Expressions in Serbian Medical Narratives

Jaćimović¹

2019

Infotheca

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“…Of the papers dealing with this problem, there are no papers directly related to the medical field. The recognition of named entities in the Serbian language was dealt with by authors in the paper [23].…”

Section: Related Workmentioning

confidence: 99%

Automated labeling of terms in medical reports in Serbian

2020

Turk J Elec Eng & Comp Sci

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Nowadays, many electronic health reports (EHRs) are stored daily. They consist of the structured part and of an unstructured section written in natural language. Due to the limited time for medical examination, EHRs are short reports which often contain errors and abbreviations. Therefore it is a challenge to process an EHR and extract knowledge from this part of the text for different purposes. This paper compares the results of three proposed methods for automatic labeling of medical terms in unstructured parts of EHRs. All words are categorized as words within the medical domain (symptoms, diagnoses, therapies, anatomy, specialties etc.) and those beyond the medical domain (numbers, places, stop words etc.). The first method is based on dictionaries of medical terms, the second on the training set, and the third on the training set and rules. The results of application of different methodologies to reduce a word to its basic form (pure, prefix, stem) are given for each of the methods. The paper shows that in labeling medical terms, the methods based on medical dictionaries (diagnosis, symptoms, medications etc.) do not produce best results, therefore it is better to use manually annotated part of the data set as a model. A significant number of words (17.36%) in medical reports are abbreviations and errors, so for better results, we should focus on creating rules to solve this problem. Better results are obtained for supervised methods compared to the dictionary-based method (with relative improvement of 42.82%). The inclusion of the algorithm for processing errors and abbreviations increased the results (with a relative improvement of 4.21%) and gave the largest F1-measure (0.9082). The advantage of the proposed method is that the use of rules for processing errors and abbreviations provides good results regardless of how the word is reduced to its basic form.

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“…The normalization of medical reports in the Serbian language is presented in [11]. The authors deal with the recognition of named entities in the Serbian language [12].…”

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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A system for named entity recognition based on local grammars

Cited by 16 publications

References 2 publications

Recognition and Normalization of Temporal Expressions in Serbian Medical Narratives

Recognition and Normalization of Temporal Expressions in Serbian Medical Narratives

Automated labeling of terms in medical reports in Serbian

Creating Resources for Marking Diagnoses in Electronic Health Reports in Serbian

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