1.
Alfalahi, Alyaa, et al.
(författare)
Expanding a dictionary of marker words for uncertainty and negation using distributional semantics
2015
Ingår i: EMNLP 2015 - 6th International Workshop on Health Text Mining and Information Analysis, LOUHI 2015 : Proceedings of the Workshop - Proceedings of the Workshop. - : Association for Computational Linguistics. - 9781941643327 ; , s. 90-96
Konferensbidrag (refereegranskat) abstract
Approaches to determining the factuality of diagnoses and findings in clinical text tend to rely on dictionaries of marker words for uncertainty and negation. Here, a method for semi-automatically expanding a dictionary of marker words using distributional semantics is presented and evaluated. It is shown that ranking candidates for inclusion according to their proximity to cluster centroids of semantically similar seed words is more successful than ranking them according to proximity to each individual seed word.
2.
Henriksson, Aron, et al.
(författare)
Synonym Extraction of Medical Terms from Clinical Text Using Combinations of Word Space Models
2012
Ingår i: Proceedings of the 5th International Symposium on Semantic Mining in Biomedicine (SMBM 2012). - 9783033038233 ; 2012, s. 10-17
Konferensbidrag (refereegranskat) abstract
In information extraction, it is useful to know if two signifiers have the same or very similar semantic content. Maintaining such information in a controlled vocabulary is, however, costly. Here it is demonstrated how synonyms of medical terms can be extracted automatically from a large corpus of clinical text using distributional semantics. By combining Random Indexing and Random Permutation, different lexical semantic aspects are captured, effectively increasing our ability to identify synonymic relations between terms. 44% of 340 synonym pairs from MeSH are successfully extracted in a list of ten suggestions. The models can also be used to map abbreviations to their full-length forms; simple pattern-based filtering of the suggestions yields substantial improvements.
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).
4.
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.
5.
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.
6.
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.
7.
Dalianis, Hercules, et al.
(författare)
Stockholm EPR Corpus : A Clinical Database Used to Improve Health Care
2012
Ingår i: Proceedings of SLCT 2012. ; , s. 17-18
Konferensbidrag (övrigt vetenskapligt/konstnärligt) abstract
The care of patients is well documented in health records. Despite being a valuable source of information that could be mined by computers and used to improve health care, health records are not readily available for research. Moreover, the narrative parts of the records are noisy and need to be interpreted by domain experts. In this abstract we describe our experiences of gaining access to a database of electronic health records for research. We also highlight some important issues in this domain and describe a number of possible applications, including comorbidity networks, detection of hospital-acquired infections and adverse drug reactions, as well as diagnosis coding support.
8.
Franch, Xavier, et al.
(författare)
Data-Driven Agile Requirements Elicitation through the Lenses of Situational Method Engineering
2021
Ingår i: 2021 IEEE 29th International Requirements Engineering Conference (RE). - : IEEE. - 9781665428569 ; , s. 402-407
Konferensbidrag (refereegranskat) abstract
Ubiquitous digitalization has led to the continuous generation of large amounts of digital data, both in organizations and in society at large. In the requirements engineering community, there has been a growing interest in considering digital data as new sources for requirements elicitation, in addition to stake-holders. The volume, dynamics, and variety of data makes iterative requirements elicitation increasingly continuous, but also unstructured and complex, which current agile methods are unable to consider and manage in a systematic and efficient manner. There is also the need to support software evolution by enabling a synergy of stakeholder-driven requirements elicitation and management with data-driven approaches. In this study, we propose extension of agile requirements elicitation by applying situational method engineering. The research is grounded on two studies in the business domains of video games and online banking.
9.
Hassel, Martin, et al.
(författare)
Something Old, Something New : Applying a Pre-trained Parsing Model to Clinical Swedish
2011
Ingår i: 18th Nordic Conference of Computational Linguistics NODALIDA 2011. - Riga, Latvia : Northern European Association for Language Technology (NEALT).
Konferensbidrag (refereegranskat) abstract
Information access from clinical text is a research area which has gained a large amount of interest in recent years. Automatic syntactic analysis for the creation of deeper language models is potentially very useful for such methods. However, syntactic parsers that are tailored to accommodate for the distinctive properties of clinical language are rare and costly to build. We present an initial study on the applicability of an existing parser, pre-trained on general Swedish, to clinical text in Swedish. We manually evaluate twelve documents and obtain a 92.4% part-of-speech tagging accuracy and a 76.6% labeled attachment score for the syntactic dependency parsing.
10.
Henriksson, Aron, et al.
(författare)
A Data-Driven Framework for Automated Requirements Elicitation from Heterogeneous Digital Sources
2020
Ingår i: The Practice of Enterprise Modeling. - Cham : Springer. - 9783030634780 - 9783030634797 ; , s. 351-365
Konferensbidrag (refereegranskat) abstract
Increased digitalization and the pervasiveness of Big Data, along with vastly improved data processing capabilities, have led to the consideration of digital data as additional sources of system requirements, complementing conventional stakeholder-driven approaches. The volume, velocity and variety of these digital sources present numerous challenges which existing system development methods are unable to manage in a systematic and efficient manner. We propose a holistic and data-driven framework for continuous and automated acquisition, analysis and aggregation of heterogeneous digital sources for the purposes of requirements elicitation and management. The proposed framework includes a conceptualization in the form of a meta-model and a high-level process for its use; the framework is illustrated in a real case of an enterprise software.
