MorphoSaurus

Schulz, Stefan; Hahn, U.; Markó, Kornél G.

doi:10.1055/s-0038-1634005

Cited by 16 publications

(2 citation statements)

References 21 publications

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“…With the issues of information overload in hospitals and the general need for research and improvements in clinical care, several IR systems have been developed specifically for health records and clinical text. Examples are the Electronic Medical Record Search Engine (EMERSE) [ 24 ], the StarTracker [ 25 ], the Queriable Patient Inference Dossier (QPID) [ 26 ], the MorphoSaurus [ 27 ], and the CISearch [ 28 ]. These software are used by clinicians and researchers to seek information from EHRs.…”

Section: Related Workmentioning

confidence: 99%

Care episode retrieval: distributional semantic models for information retrieval in the clinical domain

Moen

Ginter

Marsi

et al. 2015

BMC Med Inform Decis Mak

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Patients' health related information is stored in electronic health records (EHRs) by health service providers. These records include sequential documentation of care episodes in the form of clinical notes. EHRs are used throughout the health care sector by professionals, administrators and patients, primarily for clinical purposes, but also for secondary purposes such as decision support and research. The vast amounts of information in EHR systems complicate information management and increase the risk of information overload. Therefore, clinicians and researchers need new tools to manage the information stored in the EHRs. A common use case is, given a - possibly unfinished - care episode, to retrieve the most similar care episodes among the records. This paper presents several methods for information retrieval, focusing on care episode retrieval, based on textual similarity, where similarity is measured through domain-specific modelling of the distributional semantics of words. Models include variants of random indexing and the semantic neural network model word2vec. Two novel methods are introduced that utilize the ICD-10 codes attached to care episodes to better induce domain-specificity in the semantic model. We report on experimental evaluation of care episode retrieval that circumvents the lack of human judgements regarding episode relevance. Results suggest that several of the methods proposed outperform a state-of-the art search engine (Lucene) on the retrieval task.

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Section: Related Workmentioning

confidence: 99%

Care episode retrieval: distributional semantic models for information retrieval in the clinical domain

Moen

Ginter

Marsi

et al. 2015

BMC Med Inform Decis Mak

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“…In addition, the quality of the translations available for medical documents can be an issue [2]. In the past decade, the issue of access to biomedical information for non-native English speakers has been addressed through cross-language information retrieval [3-5], assuming that the target users had some level of competence in English: queries can be issued in the user’s native language to retrieve documents in English from MEDLINE® or other sources. Related work addressed the development of linguistic resources (e.g.…”

Section: Introductionmentioning

confidence: 99%

Combining MEDLINE and publisher data to create parallel corpora for the automatic translation of biomedical text

2013

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BackgroundMost of the institutional and research information in the biomedical domain is available in the form of English text. Even in countries where English is an official language, such as the United States, language can be a barrier for accessing biomedical information for non-native speakers. Recent progress in machine translation suggests that this technique could help make English texts accessible to speakers of other languages. However, the lack of adequate specialized corpora needed to train statistical models currently limits the quality of automatic translations in the biomedical domain.ResultsWe show how a large-sized parallel corpus can automatically be obtained for the biomedical domain, using the MEDLINE database. The corpus generated in this work comprises article titles obtained from MEDLINE and abstract text automatically retrieved from journal websites, which substantially extends the corpora used in previous work. After assessing the quality of the corpus for two language pairs (English/French and English/Spanish) we use the Moses package to train a statistical machine translation model that outperforms previous models for automatic translation of biomedical text.ConclusionsWe have built translation data sets in the biomedical domain that can easily be extended to other languages available in MEDLINE. These sets can successfully be applied to train statistical machine translation models. While further progress should be made by incorporating out-of-domain corpora and domain-specific lexicons, we believe that this work improves the automatic translation of biomedical texts.

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