1.
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.
2.
Grigonyté, Gintaré, et al.
(författare)
Improving Readability of Swedish Electronic Health Records through Lexical Simplification : First Results
2014
Ingår i: Proceedings of the 3rd Workshop on Predicting and Improving Text Readability for Target Reader Populations (PITR). - Stroudsburg, USA : Association for Computational Linguistics. - 9781937284916 ; , s. 74-83
Konferensbidrag (refereegranskat) abstract
This paper describes part of an ongoing effort to improve the readability of Swedish electronic health records (EHRs). An EHR contains systematic documentation of a single patient’s medical history across time, entered by healthcare professionals with the purpose of enabling safe and informed care. Linguistically, medical records exemplify a highly specialised domain, which can be superficially characterised as having telegraphic sentences involving displaced or missing words, abundant abbreviations, spelling variations including misspellings, and terminology. We report results on lexical simplification of Swedish EHRs, by which we mean detecting the unknown, out-ofdictionary words and trying to resolve them either as compounded known words, abbreviations or misspellings.
3.
Tengstrand, Lisa, et al.
(författare)
EACL - Expansion of Abbreviations in CLinical text
2014
Ingår i: Proceedings of the 3rdWorkshop on Predicting and Improving Text Readability for Target Reader Population. - Stroudsburg, PA, USA : Association for Computational Linguistics. - 9781937284916
Konferensbidrag (refereegranskat) abstract
In the medical domain, especially in clinical texts, non-standard abbreviations are prevalent, which impairs readability for patients. To ease the understanding of the physicians’ notes, abbreviations need to be identified and expanded to their original forms. We present a distributional semantic approach to find candidates of the original form of the abbreviation, and combine this with Levenshtein distance to choose the correct candidate among the semantically related words. We apply the method to radiology reports and medical journal texts, and compare the results to general Swedish. The results show that the correct expansion of the abbreviation can be found in 40% of the cases, an improvement by 24 percentage points compared to the baseline (0.16), and an increase by 22 percentage points compared to using word space models alone (0.18).
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6.
Chapman, Wendy W., et al.
(författare)
Extending the NegEx Lexicon for Multiple Languages
2013
Ingår i: Proceedings of the 14th World Congress on Medical and Health Informatics. - : IOS Press. - 9781614992882 - 9781614992899 ; , s. 677-681
Konferensbidrag (refereegranskat) abstract
We translated an existing English negation lexicon (NegEx) to Swedish, French, and German and compared the lexicon on corpora from each language. We observed Zipf’s law for all languages, i.e., a few phrases occur a large number of times, and a large number of phrases occur fewer times. Negation triggers “no” and “not” were common for all languages; however, other triggers varied considerably. The lexicon is available in OWL and RDF format and can be extended to other languages. We discuss the challenges in translating negation triggers to other languages and issues in representing multilingual lexical knowledge.
7.
Dalianis, Hercules, et al.
(författare)
HEALTH BANK - A Workbench for Data Science Applications in Healthcare
2015
Ingår i: Industry Track Workshop. - : CEUR Workshop Proceedings. ; , s. 1-18
Konferensbidrag (refereegranskat) abstract
The enormous amounts of data that are generated in the healthcare process and stored in electronic health record (EHR) systems are an underutilized resource that, with the use of data science applica- tions, can be exploited to improve healthcare. To foster the development and use of data science applications in healthcare, there is a fundamen- tal need for access to EHR data, which is typically not readily available to researchers and developers. A relatively rare exception is the large EHR database, the Stockholm EPR Corpus, comprising data from more than two million patients, that has been been made available to a lim- ited group of researchers at Stockholm University. Here, we describe a number of data science applications that have been developed using this database, demonstrating the potential reuse of EHR data to support healthcare and public health activities, as well as facilitate medical re- search. However, in order to realize the full potential of this resource, it needs to be made available to a larger community of researchers, as well as to industry actors. To that end, we envision the provision of an in- frastructure around this database called HEALTH BANK – the Swedish Health Record Research Bank. It will function both as a workbench for the development of data science applications and as a data explo- ration tool, allowing epidemiologists, pharmacologists and other medical researchers to generate and evaluate hypotheses. Aggregated data will be fed into a pipeline for open e-access, while non-aggregated data will be provided to researchers within an ethical permission framework. We believe that HEALTH BANK has the potential to promote a growing industry around the development of data science applications that will ultimately increase the efficiency and effectiveness of healthcare.
8.
Ehrentraut, Claudia, et al.
(författare)
Detecting Healthcare-Associated Infections in Electronic Health Records : Evaluation of Machine Learning and Preprocessing Techniques
2014
Ingår i: Proceedings of the 6th International Symposium on Semantic Mining in Biomedicine (SMBM 2014). - : University of Aveiro. ; , s. 3-10
Konferensbidrag (refereegranskat) abstract
Healthcare-associated infections (HAI) are in- fections that patients acquire in the course of medical treatment. Being a severe pub- lic health problem, detecting and monitoring HAI in healthcare documentation is an impor- tant topic to address. Research on automated systems has increased over the past years, but performance is yet to be enhanced. The dataset in this study consists of 214 records obtained from a Point-Prevalence Survey. The records are manually classified into HAI and NoHAI records. Nine different preprocess- ing steps are carried out on the data. Two learning algorithms, Random Forest (RF) and Support Vector Machines (SVM), are applied to the data. The aim is to determine which of the two algorithms is more applicable to the task and if preprocessing methods will affect the performance. RF obtains the best performance results, yielding an F1 -score of 85% and AUC of 0.85 when lemmatisation is used as a preprocessing technique. Irrespec- tive of which preprocessing method is used, RF yields higher recall values than SVM, with a statistically significant difference for all but one preprocessing method. Regarding each classifier separately, the choice of preprocess- ing method led to no statistically significant improvement in performance results.
9.
Henriksson, Aron, et al.
(författare)
Corpus-Driven Terminology Development : Populating Swedish SNOMED CT with Synonyms Extracted from Electronic Health Records
2013
Ingår i: Proceedings of the 2013 Workshop on Biomedical Natural Language Processing (BioNLP 2013). - : Association for Computational Linguistics. - 9781937284541 ; , s. 36-44
Konferensbidrag (refereegranskat) abstract
The various ways in which one can refer to the same clinical concept needs to be accounted for in a semantic resource such as SNOMED CT. Developing terminological resources manually is, however, prohibitively expensive and likely to result in low coverage, especially given the high variability of language use in clinical text. To support this process, distributional methods can be employed in conjunction with a large corpus of electronic health records to extract synonym candidates for clinical terms. In this paper, we exemplify the potential of our proposed method using the Swedish version of SNOMED CT, which currently lacks synonyms. A medical expert inspects two thousand term pairs generated by two semantic spaces -- one of which models multiword terms in addition to single words -- for one hundred preferred terms of the semantic types disorder and finding.
10.
Henriksson, Aron, et al.
(författare)
Detecting Protected Health Information in Heterogeneous Clinical Notes
2017
Ingår i: MEDINFO 2017. - : IOS Press. - 9781614998297 - 9781614998303 ; , s. 393-397
Konferensbidrag (refereegranskat) abstract
To enable secondary use of healthcare data in a privacy-preserving manner, there is a need for methods capable of automatically identifying protected health information (PHI) in clinical text. To that end, learning predictive models from labeled examples has emerged as a promising alternative to rule-based systems. However, little is known about differences with respect to PHI prevalence in different types of clinical notes and how potential domain differences may affect the performance of predictive models trained on one particular type of note and applied to another. In this study, we analyze the performance of a predictive model trained on an existing PHI corpus of Swedish clinical notes and applied to a variety of clinical notes: written (i) in different clinical specialties, (ii) under different headings, and (iii) by persons in different professions. The results indicate that domain adaption is needed for effective detection of PHI in heterogeneous clinical notes.
