1.
Névéol, Aurélie, et al.
(författare)
Clinical Natural Language Processing in languages other than English : opportunities and challenges
2018
Ingår i: Journal of Biomedical Semantics. - : Springer Science and Business Media LLC. - 2041-1480. ; 9
Forskningsöversikt (refereegranskat) abstract
Background: Natural language processing applied to clinical text or aimed at a clinical outcome has been thriving in recent years. This paper offers the first broad overview of clinical Natural Language Processing (NLP) for languages other than English. Recent studies are summarized to offer insights and outline opportunities in this area. Main Body: We envision three groups of intended readers: (1) NLP researchers leveraging experience gained in other languages, (2) NLP researchers faced with establishing clinical text processing in a language other than English, and (3) clinical informatics researchers and practitioners looking for resources in their languages in order to apply NLP techniques and tools to clinical practice and/or investigation. We review work in clinical NLP in languages other than English. We classify these studies into three groups: (i) studies describing the development of new NLP systems or components de novo, (ii) studies describing the adaptation of NLP architectures developed for English to another language, and (iii) studies focusing on a particular clinical application. Conclusion: We show the advantages and drawbacks of each method, and highlight the appropriate application context. Finally, we identify major challenges and opportunities that will affect the impact of NLP on clinical practice and public health studies in a context that encompasses English as well as other languages.
2.
Ahltorp, Magnus, et al.
(författare)
Using text prediction for facilitating input and improving readability of clinical text
2013
Ingår i: Studies in Health Technology and Informatics. - : IOS Press. - 9781614992882 - 9781614992899 ; , s. 1149-
Konferensbidrag (refereegranskat) abstract
Text prediction has the potential for facilitating and speeding up the documentation work within health care, making it possible for health personnel to allocate less time to documentation and more time to patient care. It also offers a way to produce clinical text with fewer misspellings and abbreviations, increasing readability. We have explored how text prediction can be used for input of clinical text, and how the specific challenges of text prediction in this domain can be addressed. A text prediction prototype was constructed using data from a medical journal and from medical terminologies. This prototype achieved keystroke savings of 26% when evaluated on texts mimicking authentic clinical text. The results are encouraging, indicating that there are feasible methods for text prediction in the clinical domain.
3.
Dalianis, Hercules, et al.
(författare)
Comparing manual text patterns and machine learning for classification of e-mails for automatic answering by a government agency
2011
Ingår i: 12th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2011. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 9783642194368 ; , s. 234-243
Konferensbidrag (refereegranskat) abstract
E-mails to government institutions as well as to large companies may contain a large proportion of queries that can be answered in a uniform way. We analysed and manually annotated 4,404 e-mails from citizens to the Swedish Social Insurance Agency, and compared two methods for detecting answerable e-mails: manually-created text patterns (rule-based) and machine learning-based methods. We found that the text pattern-based method gave much higher precision at 89 percent than the machine learning-based method that gave only 63 percent precision. The recall was slightly higher (66 percent) for the machine learning-based methods than for the text patterns (47 percent). We also found that 23 percent of the total e-mail flow was processed by the automatic e-mail answering system.
4.
Dalianis, Hercules, et al.
(författare)
Creating a reusable English-Chinese parallel corpus for bilingual dictionary construction
2010
Ingår i: Proceedings of the 7th International Conference on Language Resources and Evaluation, LREC 2010. - : European Language Resources Association (ELRA). - 2951740867 - 9782951740860 ; , s. 1700-1705
Konferensbidrag (refereegranskat) abstract
This paper first describes an experiment to construct an English-Chinese parallel corpus, then applying the Uplug word alignment tool on the corpus and finally produce and evaluate an English-Chinese word list. The Stockholm English-Chinese Parallel Corpus (SEC) was created by downloading English-Chinese parallel corpora from a Chinese web site containing law texts that have been manually translated from Chinese to English. The parallel corpus contains 104 563 Chinese characters equivalent to 59 918 Chinese words, and the corresponding English corpus contains 75 766 English words. However Chinese writing does not utilize any delimiters to mark word boundaries so we had to carry out word segmentation as a preprocessing step on the Chinese corpus. Moreover since the parallel corpus is downloaded from Internet the corpus is noisy regarding to alignment between corresponding translated sentences. Therefore we used 60 hours of manually work to align the sentences in the English and Chinese parallel corpus before performing automatic word alignment using Uplug. The word alignment with Uplug was carried out from English to Chinese. Nine respondents evaluated the resulting English-Chinese word list with frequency equal to or above three and we obtained an accuracy of 73.1 percent.
5.
Dalianis, Hercules, et al.
(författare)
Clustering e-mails for the Swedish social insurance agency - What part of the e-mail thread gives the best quality?
2010
Ingår i: Advances in Natural Language Processing. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642147692 - 9783642147708 ; , s. 115-120
Konferensbidrag (refereegranskat) abstract
We need to analyse a large number of e-mails sent by the citizens to the customer services department of a governmental organisation based in Sweden. To carry out this analysis we clustered a large number of e-mails with the aim of automatic e-mail answering. One issue that came up was whether we should use the whole e-mail including the thread or just the original query for the clustering. In this paper we describe this investigation. Our results show that only the query and the answering part should be used, but not necessarily the whole e-mail thread. The results clearly show that the original question contains more useful information than only the answer, although a combination is even better. Using the full e-mail thread does not downgrade the result.
6.
Alam, Mahbub Ul, et al.
(författare)
Deep Learning from Heterogeneous Sequences of Sparse Medical Data for Early Prediction of Sepsis
2020
Ingår i: Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies, Volume 5: HEALTHINF. - Setúbal : SciTePress. - 9789897583988 ; , s. 45-55
Konferensbidrag (refereegranskat) abstract
Sepsis is a life-threatening complication to infections, and early treatment is key for survival. Symptoms of sepsis are difficult to recognize, but prediction models using data from electronic health records (EHRs) can facilitate early detection and intervention. Recently, deep learning architectures have been proposed for the early prediction of sepsis. However, most efforts rely on high-resolution data from intensive care units (ICUs). Prediction of sepsis in the non-ICU setting, where hospitalization periods vary greatly in length and data is more sparse, is not as well studied. It is also not clear how to learn effectively from longitudinal EHR data, which can be represented as a sequence of time windows. In this article, we evaluate the use of an LSTM network for early prediction of sepsis according to Sepsis-3 criteria in a general hospital population. An empirical investigation using six different time window sizes is conducted. The best model uses a two-hour window and assumes data is missing not at random, clearly outperforming scoring systems commonly used in healthcare today. It is concluded that the size of the time window has a considerable impact on predictive performance when learning from heterogeneous sequences of sparse medical data for early prediction of sepsis.
7.
Alam, Mahbub Ul, et al.
(författare)
Terminology Expansion with Prototype Embeddings : Extracting Symptoms of Urinary Tract Infection from Clinical Text
2021
Ingår i: Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF. - Setúbal : SciTePress. - 9789897584909 ; , s. 47-57
Konferensbidrag (refereegranskat) abstract
Many natural language processing applications rely on the availability of domain-specific terminologies containing synonyms. To that end, semi-automatic methods for extracting additional synonyms of a given concept from corpora are useful, especially in low-resource domains and noisy genres such as clinical text, where nonstandard language use and misspellings are prevalent. In this study, prototype embeddings based on seed words were used to create representations for (i) specific urinary tract infection (UTI) symptoms and (ii) UTI symptoms in general. Four word embedding methods and two phrase detection methods were evaluated using clinical data from Karolinska University Hospital. It is shown that prototype embeddings can effectively capture semantic information related to UTI symptoms. Using prototype embeddings for specific UTI symptoms led to the extraction of more symptom terms compared to using prototype embeddings for UTI symptoms in general. Overall, 142 additional UTI symp tom terms were identified, yielding a more than 100% increment compared to the initial seed set. The mean average precision across all UTI symptoms was 0.51, and as high as 0.86 for one specific UTI symptom. This study provides an effective and cost-effective solution to terminology expansion with small amounts of labeled data.
8.
Alfalahi, Alyaa, et al.
(författare)
Pseudonymisation of Personal Names and other PHIs in an Annotated Clinical Swedish Corpus
2012
Ingår i: LREC 2012, Eighth International Conference on Language Resources and Evaluation. - 9782951740877
Konferensbidrag (refereegranskat) abstract
Today a large number of patient records are produced and these records contain valuable information, often in free text, about the medical treatment of patients. Since these records contain information that can reveal the identity of patients, known as protected health information (PHI), the records cannot easily be made available for the research community. In this research we have used a PHI annotated clinical corpora, written in Swedish, that we have pseudonymised. Pseudonymisation means to replace the sensitive information with fictive information for example real personal names are replaced with fictive personal names based on the gender of the real names and family relations. We have evaluated our results and our five respondents of who three were clinicians found that the clinical text looks real and is readable. We have also added pseudonymisation for telephone numbers, locations, health care units, dates and ages. In this paper we also present the entire de-identification and pseudonymisation process of a sample clinical text.
9.
Allvin, Helen, et al.
(författare)
Characteristics of Finnish and Swedish intensive care nursing narratives : a comparative analysis to support the development of clinical language technologies
2011
Ingår i: Journal of Biomedical Semantics. - 2041-1480. ; 2:S1, s. 1-11
Tidskriftsartikel (refereegranskat) abstract
Background: Free text is helpful for entering information into electronic health records, but reusing it is a challenge. The need for language technology for processing Finnish and Swedish healthcare text is therefore evident; however, Finnish and Swedish are linguistically very dissimilar. In this paper we present a comparison of characteristics in Finnish and Swedish free-text nursing narratives from intensive care. This creates a framework for characterising and comparing clinical text and lays the groundwork for developing clinical language technologies. Methods: Our material included daily nursing narratives from one intensive care unit in Finland and one in Sweden. Inclusion criteria for patients were an inpatient period of least five days and an age of at least 16 years. We performed a comparative analysis as part of a collaborative effort between Finnish- and Swedish-speaking healthcare and language technology professionals that included both qualitative and quantitative aspects. The qualitative analysis addressed the content and structure of three average- sized health records from each country. In the quantitative analysis 514 Finnish and 379 Swedish health records were studied using various language technology tools. Results: Although the two languages are not closely related, nursing narratives in Finland and Sweden had many properties in common. Both made use of specialised jargon and their content was very similar. However, many of these characteristics were challenging regarding development of language technology to support producing and using clinical documentation. Conclusions: The way Finnish and Swedish intensive care nursing was documented, was not country or language dependent, but shared a common context, principles and structural features and even similar vocabulary elements. Technology solutions are therefore likely to be applicable to a wider range of natural languages, but they need linguistic tailoring. Availability: The Finnish and Swedish data can be found at: http://www.dsv.su.se/ hexanord/data/
10.
Berg, Hanna, et al.
(författare)
Augmenting a De-identification System for Swedish Clinical Text Using Open Resources and Deep Learning
2019
Ingår i: Proceedings of the Workshop on NLP and Pseudonymisation. - Linköping : Linköping University Electronic Press. - 9789179299965 ; , s. 8-15
Konferensbidrag (refereegranskat) abstract
Electronic patient records are produced in abundance every day and there is a demand to use them for research or management purposes. The records, however, contain information in the free text that can identify the patient and therefore tools are needed to identify this sensitive information. The aim is to compare two machine learning algorithms, Long Short-Term Memory (LSTM) and Conditional Random Fields (CRF) applied to a Swedish clinical data set annotated for de-identification. The results show that CRF performs better than deep learning with LSTM, with CRF giving the best results with an F1 score of 0.91 when adding more data from within the same domain. Adding general open data did, on the other hand, not improve the results.
