53 research outputs found
TOWARDS ENHANCED E-COLLABORATION IN ACADEMIA A HOLISTIC MODEL FOR DEVELOPMENT OF E-COLLABORATION SOFTWARE
Henriksson, Aron. Neculau, Andrei. 2008. Towards Enhanced E-collaboration in Academia. A Holistic Model for Development E-collaboration Software. The Royal Institute of Technology, Stockholm, Sweden. Information and Communication Technology.E-collaboration is an inherently complex activity that encompasses many factors that supplement the pivotal technical elements. This paper investigates the various aspects of e-collaboration from an academic viewpoint, and reiterates the call for a holistic approach towards e-collaboration research and development. Moreover, the use of collaboration tools by IT students is surveyed, which substantiates the belief that e-collaboration needs to be further promoted in academia. We present a conceptual model that hopefully can provide some guidance for further research on e-collaboration and development of e-collaboration suites.E-collaboration, Academia, Requirements, Boundaries, Holistic
Something Old, Something New â Applying a Pre-trained Parsing Model to Clinical Swedish
Proceedings of the 18th Nordic Conference of Computational Linguistics
NODALIDA 2011.
Editors: Bolette Sandford Pedersen, Gunta NeĆĄpore and Inguna SkadiĆa.
NEALT Proceedings Series, Vol. 11 (2011), 287-290.
© 2011 The editors and contributors.
Published by
Northern European Association for Language
Technology (NEALT)
http://omilia.uio.no/nealt .
Electronically published at
Tartu University Library (Estonia)
http://hdl.handle.net/10062/1695
ALIGNMENT OF BUSINESS AND IS/IT STRATEGY AT TELENOR SWEDEN
Neculau, Andrei. Habib, Stephanie. Henriksson, Aron. Magarian Kenaraki, Miganoush Katrin. Liu, Yuanchang. 2009. Alignment of Business and IS/IT Strategy at Telenor Sweden.strategic alignment, IS/IT strategy, business strategy, organizational strategy, case study, Telenor
Data-driven agile requirements elicitation through the lenses of situational method engineering
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.The work presented in this paper is partially funded by the DOGO4ML Spanish research project, PID2020-117191RB-I00.Peer ReviewedPostprint (author's final draft
The augmented value of using clinical notes in semi-automated surveillance of deep surgical site infections after colorectal surgery
BACKGROUND: In patients who underwent colorectal surgery, an existing semi-automated surveillance algorithm based on structured data achieves high sensitivity in detecting deep surgical site infections (SSI), however, generates a significant number of false positives. The inclusion of unstructured, clinical narratives to the algorithm may decrease the number of patients requiring manual chart review. The aim of this study was to investigate the performance of this semi-automated surveillance algorithm augmented with a natural language processing (NLP) component to improve positive predictive value (PPV) and thus workload reduction (WR). METHODS: Retrospective, observational cohort study in patients who underwent colorectal surgery from January 1, 2015, through September 30, 2020. NLP was used to detect keyword counts in clinical notes. Several NLP-algorithms were developed with different count input types and classifiers, and added as component to the original semi-automated algorithm. Traditional manual surveillance was compared with the NLP-augmented surveillance algorithms and sensitivity, specificity, PPV and WR were calculated. RESULTS: From the NLP-augmented models, the decision tree models with discretized counts or binary counts had the best performance (sensitivity 95.1% (95%CI 83.5-99.4%), WR 60.9%) and improved PPV and WR by only 2.6% and 3.6%, respectively, compared to the original algorithm. CONCLUSIONS: The addition of an NLP component to the existing algorithm had modest effect on WR (decrease of 1.4-12.5%), at the cost of sensitivity. For future implementation it will be a trade-off between optimal case-finding techniques versus practical considerations such as acceptability and availability of resources
Predictive modeling of structured electronic health records for adverse drug event detection
Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease
Background: Experimental and clinical data suggest that reducing inflammation without affecting lipid levels may reduce the risk of cardiovascular disease. Yet, the inflammatory hypothesis of atherothrombosis has remained unproved. Methods: We conducted a randomized, double-blind trial of canakinumab, a therapeutic monoclonal antibody targeting interleukin-1ÎČ, involving 10,061 patients with previous myocardial infarction and a high-sensitivity C-reactive protein level of 2 mg or more per liter. The trial compared three doses of canakinumab (50 mg, 150 mg, and 300 mg, administered subcutaneously every 3 months) with placebo. The primary efficacy end point was nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. RESULTS: At 48 months, the median reduction from baseline in the high-sensitivity C-reactive protein level was 26 percentage points greater in the group that received the 50-mg dose of canakinumab, 37 percentage points greater in the 150-mg group, and 41 percentage points greater in the 300-mg group than in the placebo group. Canakinumab did not reduce lipid levels from baseline. At a median follow-up of 3.7 years, the incidence rate for the primary end point was 4.50 events per 100 person-years in the placebo group, 4.11 events per 100 person-years in the 50-mg group, 3.86 events per 100 person-years in the 150-mg group, and 3.90 events per 100 person-years in the 300-mg group. The hazard ratios as compared with placebo were as follows: in the 50-mg group, 0.93 (95% confidence interval [CI], 0.80 to 1.07; P = 0.30); in the 150-mg group, 0.85 (95% CI, 0.74 to 0.98; P = 0.021); and in the 300-mg group, 0.86 (95% CI, 0.75 to 0.99; P = 0.031). The 150-mg dose, but not the other doses, met the prespecified multiplicity-adjusted threshold for statistical significance for the primary end point and the secondary end point that additionally included hospitalization for unstable angina that led to urgent revascularization (hazard ratio vs. placebo, 0.83; 95% CI, 0.73 to 0.95; P = 0.005). Canakinumab was associated with a higher incidence of fatal infection than was placebo. There was no significant difference in all-cause mortality (hazard ratio for all canakinumab doses vs. placebo, 0.94; 95% CI, 0.83 to 1.06; P = 0.31). Conclusions: Antiinflammatory therapy targeting the interleukin-1ÎČ innate immunity pathway with canakinumab at a dose of 150 mg every 3 months led to a significantly lower rate of recurrent cardiovascular events than placebo, independent of lipid-level lowering. (Funded by Novartis; CANTOS ClinicalTrials.gov number, NCT01327846.
Ensembles of Semantic Spaces : On Combining Models of Distributional Semantics with Applications in Healthcare
Distributional semantics allows models of linguistic meaning to be derived from observations of language use in large amounts of text. By modeling the meaning of words in semantic (vector) space on the basis of co-occurrence information, distributional semantics permits a quantitative interpretation of (relative) word meaning in an unsupervised setting, i.e., human annotations are not required. The ability to obtain inexpensive word representations in this manner helps to alleviate the bottleneck of fully supervised approaches to natural language processing, especially since models of distributional semantics are data-driven and hence agnostic to both language and domain. All that is required to obtain distributed word representations is a sizeable corpus; however, the composition of the semantic space is not only affected by the underlying data but also by certain model hyperparameters. While these can be optimized for a specific downstream task, there are currently limitations to the extent the many aspects of semantics can be captured in a single model. This dissertation investigates the possibility of capturing multiple aspects of lexical semantics by adopting the ensemble methodology within a distributional semantic framework to create ensembles of semantic spaces. To that end, various strategies for creating the constituent semantic spaces, as well as for combining them, are explored in a number of studies. The notion of semantic space ensembles is generalizable across languages and domains; however, the use of unsupervised methods is particularly valuable in low-resource settings, in particular when annotated corpora are scarce, as in the domain of Swedish healthcare. The semantic space ensembles are here empirically evaluated for tasks that have promising applications in healthcare. It is shown that semantic space ensembles â created by exploiting various corpora and data types, as well as by adjusting model hyperparameters such as the size of the context window and the strategy for handling word order within the context window â are able to outperform the use of any single constituent model on a range of tasks. The semantic space ensembles are used both directly for k-nearest neighbors retrieval and for semi-supervised machine learning. Applying semantic space ensembles to important medical problems facilitates the secondary use of healthcare data, which, despite its abundance and transformative potential, is grossly underutilized.At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 4 and 5: Unpublished conference papers.High-Performance Data Mining for Drug Effect Detectio
Semantic Spaces of Clinical Text : Leveraging Distributional Semantics for Natural Language Processing of Electronic Health Records
The large amounts of clinical data generated by electronic health record systems are an underutilized resource, which, if tapped, has enormous potential to improve health care. Since the majority of this data is in the form of unstructured text, which is challenging to analyze computationally, there is a need for sophisticated clinical language processing methods. Unsupervised methods that exploit statistical properties of the data are particularly valuable due to the limited availability of annotated corpora in the clinical domain. Information extraction and natural language processing systems need to incorporate some knowledge of semantics. One approach exploits the distributional properties of language â more specifically, term co-occurrence information â to model the relative meaning of terms in high-dimensional vector space. Such methods have been used with success in a number of general language processing tasks; however, their application in the clinical domain has previously only been explored to a limited extent. By applying models of distributional semantics to clinical text, semantic spaces can be constructed in a completely unsupervised fashion. Semantic spaces of clinical text can then be utilized in a number of medically relevant applications. The application of distributional semantics in the clinical domain is here demonstrated in three use cases: (1) synonym extraction of medical terms, (2) assignment of diagnosis codes and (3) identification of adverse drug reactions. To apply distributional semantics effectively to a wide range of both general and, in particular, clinical language processing tasks, certain limitations or challenges need to be addressed, such as how to model the meaning of multiword terms and account for the function of negation: a simple means of incorporating paraphrasing and negation in a distributional semantic framework is here proposed and evaluated. The notion of ensembles of semantic spaces is also introduced; these are shown to outperform the use of a single semantic space on the synonym extraction task. This idea allows different models of distributional semantics, with different parameter configurations and induced from different corpora, to be combined. This is not least important in the clinical domain, as it allows potentially limited amounts of clinical data to be supplemented with data from other, more readily available sources. The importance of configuring the dimensionality of semantic spaces, particularly when â as is typically the case in the clinical domain â the vocabulary grows large, is also demonstrated.De stora mĂ€ngder kliniska data som genereras i patientjournalsystem Ă€r en underutnyttjad resurs med en enorm potential att förbĂ€ttra hĂ€lso- och sjukvĂ„rden. DĂ„ merparten av kliniska data Ă€r i form av ostrukturerad text, vilken Ă€r utmanande för datorer att analysera, finns det ett behov av sofistikerade metoder som kan behandla kliniskt sprĂ„k. Metoder som inte krĂ€ver mĂ€rkta exempel utan istĂ€llet utnyttjar statistiska egenskaper i datamĂ€ngden Ă€r sĂ€rskilt vĂ€rdefulla, med tanke pĂ„ den begrĂ€nsade tillgĂ„ngen till annoterade korpusar i den kliniska domĂ€nen. System för informationsextraktion och sprĂ„kbehandling behöver innehĂ„lla viss kunskap om semantik. En metod gĂ„r ut pĂ„ att utnyttja de distributionella egenskaperna hos sprĂ„k â mer specifikt, statistisk över hur termer samförekommer â för att modellera den relativa betydelsen av termer i ett högdimensionellt vektorrum. Metoden har anvĂ€nts med framgĂ„ng i en rad uppgifter för behandling av allmĂ€nna sprĂ„k; dess tillĂ€mpning i den kliniska domĂ€nen har dock endast utforskats i mindre utstrĂ€ckning. Genom att tillĂ€mpa modeller för distributionell semantik pĂ„ klinisk text kan semantiska rum konstrueras utan nĂ„gon tillgĂ„ng till mĂ€rkta exempel. Semantiska rum av klinisk text kan sedan anvĂ€ndas i en rad medicinskt relevanta tillĂ€mpningar. TillĂ€mpningen av distributionell semantik i den kliniska domĂ€nen illustreras hĂ€r i tre anvĂ€ndningsomrĂ„den: (1) synonymextraktion av medicinska termer, (2) tilldelning av diagnoskoder och (3) identifiering av lĂ€kemedelsbiverkningar. Det krĂ€vs dock att vissa begrĂ€nsningar eller utmaningar adresseras för att möjliggöra en effektiv tillĂ€mpning av distributionell semantik pĂ„ ett brett spektrum av uppgifter som behandlar sprĂ„k â bĂ„de allmĂ€nt och, i synnerhet, kliniskt â sĂ„som hur man kan modellera betydelsen av flerordstermer och redogöra för funktionen av negation: ett enkelt sĂ€tt att modellera parafrasering och negation i ett distributionellt semantiskt ramverk presenteras och utvĂ€rderas. IdĂ©n om ensembler av semantisk rum introduceras ocksĂ„; dessa övertrĂ€ffer anvĂ€ndningen av ett enda semantiskt rum för synonymextraktion. Den hĂ€r metoden möjliggör en kombination av olika modeller för distributionell semantik, med olika parameterkonfigurationer samt inducerade frĂ„n olika korpusar. Detta Ă€r inte minst viktigt i den kliniska domĂ€nen, dĂ„ det gör det möjligt att komplettera potentiellt begrĂ€nsade mĂ€ngder kliniska data med data frĂ„n andra, mer lĂ€ttillgĂ€ngliga kĂ€llor. Arbetet pĂ„visar ocksĂ„ vikten av att konfigurera dimensionaliteten av semantiska rum, i synnerhet nĂ€r vokabulĂ€ren Ă€r omfattande, vilket Ă€r vanligt i den kliniska domĂ€nen.High-Performance Data Mining for Drug Effect Detection (DADEL
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