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Automatic recognition of disorders, findings, pharmaceuticals and body structures from clinical text: An annotation and machine learning study
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Karolinska University Hospital, Sweden; Karolinska Institutet, Sweden.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Number of Authors: 4
2014 (English)In: Journal of Biomedical Informatics, ISSN 1532-0464, E-ISSN 1532-0480, Vol. 49, 148-158 p.Article in journal (Refereed) Published
Abstract [en]

Automatic recognition of clinical entities in the narrative text of health records is useful for constructing applications for documentation of patient care, as well as for secondary usage in the form of medical knowledge extraction. There are a number of named entity recognition studies on English clinical text, but less work has been carried out on clinical text in other languages. This study was performed on Swedish health records, and focused on four entities that are highly relevant for constructing a patient overview and for medical hypothesis generation, namely the entities: Disorder, Finding, Pharmaceutical Drug and Body Structure. The study had two aims: to explore how well named entity recognition methods previously applied to English clinical text perform on similar texts written in Swedish; and to evaluate whether it is meaningful to divide the more general category Medical Problem, which has been used in a number of previous studies, into the two more granular entities, Disorder and Finding. Clinical notes from a Swedish internal medicine emergency unit were annotated for the four selected entity categories, and the inter-annotator agreement between two pairs of annotators was measured, resulting in an average F-score of 0.79 for Disorder, 0.66 for Finding, 0.90 for Pharmaceutical Drug and 0.80 for Body Structure. A subset of the developed corpus was thereafter used for finding suitable features for training a conditional random fields model. Finally, a new model was trained on this subset, using the best features and settings, and its ability to generalise to held-out data was evaluated. This final model obtained an F-score of 0.81 for Disorder, 0.69 for Finding, 0.88 for Pharmaceutical Drug, 0.85 for Body Structure and 0.78 for the combined category Disorder + Finding. The obtained results, which are in line with or slightly lower than those for similar studies on English clinical text, many of them conducted using a larger training data set, show that the approaches used for English are also suitable for Swedish clinical text. However, a small proportion of the errors made by the model are less likely to occur in English text, showing that results might be improved by further tailoring the system to clinical Swedish. The entity recognition results for the individual entities Disorder and Finding show that it is meaningful to separate the general category Medical Problem into these two more granular entity types, e.g. for knowledge mining of co-morbidity relations and disorder-finding relations.

Place, publisher, year, edition, pages
2014. Vol. 49, 148-158 p.
Keyword [en]
Named entity recognition, Corpora development, Clinical text processing, Disorder, Finding, Swedish
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-106433DOI: 10.1016/j.jbi.2014.01.012ISI: 000337772200015OAI: oai:DiVA.org:su-106433DiVA: diva2:736294
Note

AuthorCount:4;

Available from: 2014-08-06 Created: 2014-08-04 Last updated: 2017-12-05Bibliographically approved
In thesis
1. Extracting Clinical Findings from Swedish Health Record Text
Open this publication in new window or tab >>Extracting Clinical Findings from Swedish Health Record Text
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Information contained in the free text of health records is useful for the immediate care of patients as well as for medical knowledge creation. Advances in clinical language processing have made it possible to automatically extract this information, but most research has, until recently, been conducted on clinical text written in English. In this thesis, however, information extraction from Swedish clinical corpora is explored, particularly focusing on the extraction of clinical findings. Unlike most previous studies, Clinical Finding was divided into the two more granular sub-categories Finding (symptom/result of a medical examination) and Disorder (condition with an underlying pathological process). For detecting clinical findings mentioned in Swedish health record text, a machine learning model, trained on a corpus of manually annotated text, achieved results in line with the obtained inter-annotator agreement figures. The machine learning approach clearly outperformed an approach based on vocabulary mapping, showing that Swedish medical vocabularies are not extensive enough for the purpose of high-quality information extraction from clinical text. A rule and cue vocabulary-based approach was, however, successful for negation and uncertainty classification of detected clinical findings. Methods for facilitating expansion of medical vocabulary resources are particularly important for Swedish and other languages with less extensive vocabulary resources. The possibility of using distributional semantics, in the form of Random indexing, for semi-automatic vocabulary expansion of medical vocabularies was, therefore, evaluated. Distributional semantics does not require that terms or abbreviations are explicitly defined in the text, and it is, thereby, a method suitable for clinical corpora. Random indexing was shown useful for extending vocabularies with medical terms, as well as for extracting medical synonyms and abbreviation dictionaries.

Place, publisher, year, edition, pages
Stockholm University: Department of Computer and Systems Sciences, Stockholm University, 2014. 128 p.
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 15-001
Keyword
Named entity recognition, Corpora development, Clinical text processing, Distributional semantics, Random indexing, Vocabulary expansion, Assertion classification, Clinical text mining, Electronic health records, Swedish
National Category
Information Systems, Social aspects
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-109254 (URN)978-91-7649-054-9 (ISBN)
Public defence
2015-01-23, Lilla hörsalen, NOD-huset, Borgarfjordsgatan 12, Kista, 13:00 (English)
Opponent
Supervisors
Available from: 2014-12-29 Created: 2014-11-17 Last updated: 2014-11-21Bibliographically approved

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