| 1. |
- Chomutare, Taridzo, et al.
(författare)
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De-Identifying Swedish EHR Text Using Public Resources in the General Domain
- 2020
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Ingår i: Digital Personalized Health and Medicine. - Amsterdam : IOS Press. - 9781643680828 - 9781643680835 ; , s. 148-152
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Konferensbidrag (refereegranskat)abstract
- Sensitive data is normally required to develop rule-based or train machine learning-based models for de-identifying electronic health record (EHR) clinical notes; and this presents important problems for patient privacy. In this study, we add non-sensitive public datasets to EHR training data; (i) scientific medical text and (ii) Wikipedia word vectors. The data, all in Swedish, is used to train a deep learning model using recurrent neural networks. Tests on pseudonymized Swedish EHR clinical notes showed improved precision and recall from 55.62% and 80.02% with the base EHR embedding layer, to 85.01% and 87.15% when Wikipedia word vectors are added. These results suggest that non-sensitive text from the general domain can be used to train robust models for de-identifying Swedish clinical text; and this could be useful in cases where the data is both sensitive and in low-resource languages.
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| 2. |
- Divine, Dmitri, et al.
(författare)
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Thousand years of winter surface air temperature variations in Svalbard and northern Norway reconstructed from ice-core data
- 2011
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Ingår i: Polar Research. - : Norwegian Polar Institute. - 0800-0395 .- 1751-8369. ; 30, s. 7379-
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Tidskriftsartikel (refereegranskat)abstract
- Two isotopic ice core records from western Svalbard are calibrated to reconstruct more than 1000 years of past winter surface air temperature variations in Longyearbyen, Svalbard, and Vardo, northern Norway. Analysis of the derived reconstructions suggests that the climate evolution of the last millennium in these study areas comprises three major sub-periods. The cooling stage in Svalbard (ca. 800-1800) is characterized by a progressive winter cooling of approximately 0.9 degrees C century(-1) (0.38 degrees C century(-1) for Vardo) and a lack of distinct signs of abrupt climate transitions. This makes it difficult to associate the onset of the Little Ice Age in Svalbard with any particular time period. During the 1800s, which according to our results was the coldest century in Svalbard, the winter cooling associated with the Little Ice Age was on the order of 4 degrees C (1.3 degrees C for Vardo) compared to the 1900s. The rapid warming that commenced at the beginning of the 20th century was accompanied by a parallel decline in sea-ice extent in the study area. However, both the reconstructed winter temperatures as well as indirect indicators of summer temperatures suggest the Medieval period before the 1200s was at least as warm as at the end of the 1990s in Svalbard.
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| 3. |
- Tayefi, Maryam, et al.
(författare)
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Challenges and opportunities beyond structured data in analysis of electronic health records
- 2021
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Ingår i: Wiley Interdisciplinary Reviews. - : Wiley. - 1939-5108 .- 1939-0068. ; 13:6
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Forskningsöversikt (refereegranskat)abstract
- Electronic health records (EHR) contain a lot of valuable information about individual patients and the whole population. Besides structured data, unstructured data in EHRs can provide extra, valuable information but the analytics processes are complex, time-consuming, and often require excessive manual effort. Among unstructured data, clinical text and images are the two most popular and important sources of information. Advanced statistical algorithms in natural language processing, machine learning, deep learning, and radiomics have increasingly been used for analyzing clinical text and images. Although there exist many challenges that have not been fully addressed, which can hinder the use of unstructured data, there are clear opportunities for well-designed diagnosis and decision support tools that efficiently incorporate both structured and unstructured data for extracting useful information and provide better outcomes. However, access to clinical data is still very restricted due to data sensitivity and ethical issues. Data quality is also an important challenge in which methods for improving data completeness, conformity and plausibility are needed. Further, generalizing and explaining the result of machine learning models are important problems for healthcare, and these are open challenges. A possible solution to improve data quality and accessibility of unstructured data is developing machine learning methods that can generate clinically relevant synthetic data, and accelerating further research on privacy preserving techniques such as deidentification and pseudonymization of clinical text.
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