| 1. |
- Ahltorp, Magnus, et al.
(författare)
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Using text prediction for facilitating input and improving readability of clinical text
- 2013
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Ingår i: Studies in Health Technology and Informatics. - : IOS Press. - 9781614992882 - 9781614992899 ; , s. 1149-
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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.
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| 2. |
- Alam, Mahbub Ul, et al.
(författare)
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Deep Learning from Heterogeneous Sequences of Sparse Medical Data for Early Prediction of Sepsis
- 2020
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Ingår i: Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies, Volume 5: HEALTHINF. - Setúbal : SciTePress. - 9789897583988 ; , s. 45-55
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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.
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| 3. |
- Alam, Mahbub Ul, et al.
(författare)
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Terminology Expansion with Prototype Embeddings : Extracting Symptoms of Urinary Tract Infection from Clinical Text
- 2021
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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
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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.
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| 4. |
- Alfalahi, Alyaa, et al.
(författare)
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Pseudonymisation of Personal Names and other PHIs in an Annotated Clinical Swedish Corpus
- 2012
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Ingår i: LREC 2012, Eighth International Conference on Language Resources and Evaluation. - 9782951740877
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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.
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| 5. |
- Allvin, Helen, et al.
(författare)
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Characteristics of Finnish and Swedish intensive care nursing narratives : a comparative analysis to support the development of clinical language technologies
- 2011
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Ingår i: Journal of Biomedical Semantics. - 2041-1480. ; 2:S1, s. 1-11
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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/
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| 6. |
- Andrenucci, Andrea, et al.
(författare)
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Knowledge patterns for online health portal development
- 2019
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Ingår i: Health Informatics Journal. - : SAGE Publications. - 1460-4582 .- 1741-2811. ; 25:4, s. 1779-1799
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Tidskriftsartikel (refereegranskat)abstract
- This article describes the development and evaluation of a set of knowledge patterns that provide guidelines and implications of design for developers of mental health portals. The knowledge patterns were based on three foundations: 1) Knowledge integration of language technology approaches; 2) Experiments with language technology applications and 3) User studies of portal interaction. A mixed-methods approach was employed for the evaluation of the knowledge patterns: formative workshops with knowledge pattern experts and summative surveys with experts in specific domains. The formative evaluation improved the cohesion of the patterns. The results of the summative evaluation showed that the problems discussed in the patterns were relevant for the domain and that the knowledge embedded was useful to solve them. Ten patterns out of thirteen achieved an average score above 4.0, which is a positive result that leads us to conclude that they can be used as guidelines for developing health portals.
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| 7. |
- Andrenucci, Andrea, 1971-
(författare)
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Using Language Technology to Mediate Medical Information on Health Portals : User Studies and Experiments
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The World Wide Web has revolutionized our lifestyle, our economies and services within health care. Health care services are no longer provided only at specialist centers and at scheduled hours, but also through online tools that give health care consumers access to medical information, health records, medical counselling and peer support. Such tools and applications are generally available on larger web sites or gateways called health portals. A large majority of online medical information consumers are laypeople (i.e. non experts) who appreciate the possibility to submit their information needs in their own native language. The information retrieval process where information requests from users and retrieved documents/answers are in different languages is called cross-language information retrieval (CLIR). Mental health is one of the medical areas where some online applications have been successfully deployed in order to help people by providing in-depth medical information, counseling and advice. Despite the fact that online health portals are considered priority e-health tools for improving mental health, there are no formal knowledge instruments such as knowledge patterns that explicitly support the development of online health portals in the field of psychology/psychotherapy. The goal of this research is to produce and evaluate a set of knowledge patterns, for the development and implementation of cross-lingual online health portals aimed at information seekers without medical expertise in the domain of psychology and psychotherapy. The knowledge patterns synthetize results of three research foundations: 1) User studies of portal interaction, based on interviews and observations about how users experience health information online and personalized search 2) Knowledge integration of existing language technology approaches, and 3) Experiments with language technology applications, in the field of cross-lingual information retrieval/question-answering. The target groups of this research are developers, researchers and health care providers, i.e. people who are responsible for mediating medical information on online health portals for users without medical expertise. The chosen research framework is design science, i.e. the science that focuses on the study, development and evaluation of artefacts (objects that help people solve a practical problem). Typical examples of artefacts in IT are algorithms, software solutions and databases, but also objects such as processes or knowledge patterns. The developed and evaluated artefact in this research is a set of knowledge patterns for online health portal development. The developed artefact contains fourteen knowledge patterns covering the three research foundations. Formative (structured workshops) and summative (online survey) evaluation of the artefact indicate that the knowledge patterns are useful, relevant and adoptable to a large extent, they also provide further directions for development of online mental health portals. Developing portals with multilingual support and tailored interfaces has the potential of helping larger groups of citizens to access relevant medical information.
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| 8. |
- Bampa, Maria, et al.
(författare)
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Detecting Adverse Drug Events from Swedish Electronic Health Records using Text Mining
- 2020
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Ingår i: Proceedings of the LREC 2020 Workshop on Multilingual Biomedical Text Processing (MultilingualBIO 2020). - : European Language Resources Association. - 9791095546658 ; , s. 1-8
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Konferensbidrag (refereegranskat)abstract
- Electronic Health Records are a valuable source of patient information which can be leveraged to detect Adverse Drug Events (ADEs) and aid post-mark drug-surveillance. The overall aim of this study is to scrutinize text written by clinicians in the EHRs and build a model for ADE detection that produces medically relevant predictions. Natural Language Processing techniques will be exploited to create important predictors and incorporate them into the learning process. The study focuses on the 5 most frequent ADE cases found ina Swedish electronic patient record corpus. The results indicate that considering textual features, rather than the structured, can improve the classification performance by 15{\%} in some ADE cases. Additionally, variable patient history lengths are incorporated in the models, demonstrating the importance of the above decision rather than using an arbitrary number for a history length. The experimental findings suggest that the clinical text in EHRs includes information that can capture data beyond the ones that are found in a structured format.
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| 9. |
- Berg, Hanna, et al.
(författare)
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A Semi-supervised Approach for De-identification of Swedish Clinical Text
- 2020
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Ingår i: Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020). - : European Language Resources Association. - 9791095546344 ; , s. 4444-4450
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Konferensbidrag (refereegranskat)abstract
- An abundance of electronic health records (EHR) is produced every day within healthcare. The records possess valuable information for research and future improvement of healthcare. Multiple efforts have been done to protect the integrity of patients while making electronic health records usable for research by removing personally identifiable information in patient records. Supervised machine learning approaches for de-identification of EHRs need annotated data for training, annotations that are costly in time and human resources. The annotation costs for clinical text is even more costly as the process must be carried out in a protected environment with a limited number of annotators who must have signed confidentiality agreements. In this paper is therefore, a semi-supervised method proposed, for automatically creating high-quality training data. The study shows that the method can be used to improve recall from 84.75% to 89.20% without sacrificing precision to the same extent, dropping from 95.73% to 94.20%. The model’s recall is arguably more important for de-identification than precision.
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| 10. |
- Berg, Hanna, et al.
(författare)
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Augmenting a De-identification System for Swedish Clinical Text Using Open Resources and Deep Learning
- 2019
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Ingår i: Proceedings of the Workshop on NLP and Pseudonymisation. - Linköping : Linköping University Electronic Press. - 9789179299965 ; , s. 8-15
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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.
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