Automatic Annotation of Medical Records in Spanish with Disease, Drug and Substance Names

Oronoz, Maite; Casillas, Arantza; Gojenola, Koldo; Pérez, Alicia

doi:10.1007/978-3-642-41827-3_67

Cited by 24 publications

(15 citation statements)

References 7 publications

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“…Next we adapted the linguistic analyzer FreeLing [7] to the domain of medicine by enriching its dictionaries with medical terms, obtaining FreeLing-Med. Later, we evaluated FreeLing-Med against the preliminary annotated corpus [26]. 3.…”

Section: Methodsmentioning

confidence: 99%

“…We developed FreeLing-Med, a toolkit to analyze documents in the biomedical domain in Spanish and English [26,27]. The resulting domain-adapted FreeLingMed system is able to carry out medical named entity recognition, linking of all the terms in SNOMED CT with their corresponding semantic tag (substances, disorders, procedures, findings), and identification of all the medical abbreviations using the dictionary by Yetano and Alberola [28] as well as the identification of brand-drug names using a drug database called BOTPLUS 5 .…”

Section: Freeling-medmentioning

confidence: 99%

See 1 more Smart Citation

On the creation of a clinical gold standard corpus in Spanish: Mining adverse drug reactions

Oronoz¹,

Gojenola²,

Pérez³

et al. 2015

Journal of Biomedical Informatics

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The advances achieved in Natural Language Processing make it possible to automatically mine information from electronically created documents. Many Natural Language Processing methods that extract information from texts make use of annotated corpora, but these are scarce in the clinical domain due to legal and ethical issues. In this paper we present the creation of the IxaMed-GS gold standard composed of real electronic health records written in Spanish and manually annotated by experts in pharmacology and pharmacovigilance. The experts mainly annotated entities related to diseases and drugs, but also relationships between entities indicating adverse drug reaction events. To help the experts in the annotation task, we adapted a general corpus linguistic analyzer to the medical domain. The quality of the annotation process in the IxaMed-GS corpus has been assessed by measuring the inter-annotator agreement, which was 90.53% for entities and 82.86% for events. In addition, the corpus has been used for the automatic extraction of adverse drug reaction events using machine learning.

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Section: Methodsmentioning

confidence: 99%

Section: Freeling-medmentioning

confidence: 99%

On the creation of a clinical gold standard corpus in Spanish: Mining adverse drug reactions

Oronoz¹,

Gojenola²,

Pérez³

et al. 2015

Journal of Biomedical Informatics

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“…This morpho-syntactic information was obtained with FreeLing-Med. 19 Besides, other features were used, such as the anatomical therapeutic chemical classification (ATC/DDD index) of the drug entity, presence of trigger words (e.g. "caused by," "related with," etc.)…”

Section: Approaches To Overcome the Class Imbalancementioning

confidence: 99%

The class imbalance problem detecting adverse drug reactions in electronic health records

Santiso

Casillas

Pérez

2018

Health Informatics J

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This work focuses on adverse drug reaction extraction tackling the class imbalance problem. Adverse drug reactions are infrequent events in electronic health records, nevertheless, it is compulsory to get them documented. Text mining techniques can help to retrieve this kind of valuable information from text. The class imbalance was tackled using different sampling methods, cost-sensitive learning, ensemble learning and one-class classification and the Random Forest classifier was used. The adverse drug reaction extraction model was inferred from a dataset that comprises real electronic health records with an imbalance ratio of 1:222, this means that for each drug-disease pair that is an adverse drug reaction, there are approximately 222 that are not adverse drug reactions. The application of a sampling technique before using cost-sensitive learning offered the best result. On the test set, the f-measure was 0.121 for the minority class and 0.996 for the majority class.

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“…Each concept, as requested by the pharmacy service, should gather several sub-concepts stated as follows: These concepts were identified by means of a general purpose analyser available for Spanish, called FreeLing (Padró et al, 2010), that had been enhanced with medical ontologies and dictionaries, such as SNOMED-CT, BotPLUS, ICD-9-CM, etc. (Oronoz et al, 2013). This toolkit is able to identify multi-word context-terms, lemmas and also POS tags.…”

Section: Annotating Concepts By Shallow Analysismentioning

confidence: 99%

Adverse Drug Event prediction combining shallow analysis and machine learning

Santiso¹,

Casillas²,

Pérez³

et al. 2014

Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi)

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The aim of this work is to infer a model able to extract cause-effect relations between drugs and diseases. A two-level system is proposed. The first level carries out a shallow analysis of Electronic Health Records (EHRs) in order to identify medical concepts such as drug brandnames, substances, diseases, etc. Next, all the combination pairs formed by a concept from the group of drugs (drug and substances) and the group of diseases (diseases and symptoms) are characterised through a set of 57 features. A supervised classifier inferred on those features is in charge of deciding whether that pair represents a cause-effect type of event.One of the challenges of this work is the fact that the system explores the entire document. The contributions of this paper stand on the use of real EHRs to discover adverse drug reaction events even in different sentences. Besides, the work focuses on Spanish language.

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Automatic Annotation of Medical Records in Spanish with Disease, Drug and Substance Names

Cited by 24 publications

References 7 publications

On the creation of a clinical gold standard corpus in Spanish: Mining adverse drug reactions

On the creation of a clinical gold standard corpus in Spanish: Mining adverse drug reactions

The class imbalance problem detecting adverse drug reactions in electronic health records

Adverse Drug Event prediction combining shallow analysis and machine learning

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