| 2. |
- Kokkinakis, Dimitrios, 1965
(författare)
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Complementary Methods for De-identifying Sensitive Data with a focus on Clinical Discourse
- 2010
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Ingår i: Revista de Procesamiento de Lenguaje Natural (SEPLN). - 1135-5948. ; 45, s. 243-246
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Tidskriftsartikel (refereegranskat)abstract
- In the era of the Electronic Health Record (EHR) the release of individual data for research, public health planning, health care statistics, monitoring of diagnostic tests, automated data collection for health care registries and tracking disease outbreaks are some of the areas in which the protection of Personal Health Information (PHI) has become an important concern. The purpose of this study is to adapt and apply synergetic methods to document de-identification, particularly clinical, or other sources of sensitive data. The main challenge and goal of this research is to retain important concepts and PHI in the documents in a standardized and neutral manner as means of encryption without violating the integrity of the PHI and without sacrificing the quality and intended meaning of the authors.
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| 3. |
- Weegar, Rebecka, et al.
(författare)
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Ensembles for clinical entity extraction
- 2018
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Ingår i: Revista de Procesamiento de Lenguaje Natural (SEPLN). - 1135-5948 .- 1989-7553. ; :60, s. 13-20
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Tidskriftsartikel (refereegranskat)abstract
- Health records are a valuable source of clinical knowledge and Natural Language Processing techniques have previously been applied to the text in health records for a number of applications. Often, a first step in clinical text processing is clinical entity recognition; identifying, for example, drugs, disorders, and body parts in clinical text. However, most of this work has focused on records in English. Therefore, this work aims to improve clinical entity recognition for languages other than English by comparing the same methods on two different languages, specifically by employing ensemble methods. Models were created for Spanish and Swedish health records using SVM, Perceptron, and CRF and four different feature sets, including unsupervised features. Finally, the models were combined in ensembles. Weighted voting was applied according to the models individual F-scores. In conclusion, the ensembles improved the overall performance for Spanish and the precision for Swedish.
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