Critical care data processing tools

cleanEHR (Shi et al. 2017) is a data cleaning and wrangling platform which works with the Critical Care Health Informatics Collaborative (CCHIC) database. CCHIC collects and gathers high resolution longitudinal patient record from critical care units at Cambridge, Guys/Kings/St Thomas’, Imperial, Oxford, UCL Hospitals. The increased adoption of high resolution longitudinal EHRs has created novel opportunities for researchers, clinicians and data scientists to access large, enriched patient databases (Harrison, Brady, and Rowan 2004) (Johnson et al. 2016). The purpose of cleanEHR is to enable researchers to answer clinical questions that are important to patients. cleanEHR is a solution to address various data reliability and accessibility problems as well. It provides a platform that enables data manipulation, transformation, reduction, cleaning and validation with a friendly user interface which empowers non-programmers to conduct basic data analysis by simply writing a human-readable configuration file

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

A model-driven approach for facilitating user-friendly design of complex event patterns

Complex Event Processing (CEP) is an emerging technology which allows us to efficiently process and correlate huge amounts of data in order to discover relevant or critical situations of interest (complex events) for a specific domain. This technology requires domain experts to define complex event patterns, where the conditions to be detected are specified by means of event processing languages. However, these experts face the handicap of defining such patterns with editors which are not user-friendly enough. To solve this problem, a model-driven approach for facilitating user-friendly design of complex event patterns is proposed and developed in this paper. Besides, the proposal has been applied to different domains and several event processing languages have been compared. As a result, we can affirm that the presented approach is independent both of the domain where CEP technology has to be applied to and of the concrete event processing language required for defining event patterns

Strategic Research Agenda for organic food and farming

The TP Organics Strategic Research Agenda (SRA) was finalised in December 2009. The purpose of the Strategic Research Agenda (SRA) is to enable research, development and knowledge transfer that will deliver relevant outcomes – results that will contribute to the improvement of the organic sector and other low external input systems. The document has been developed through a dynamic consultative process that ran from 2008 to 2009. It involved a wide range of stakeholders who enthusiastically joined the effort to define organic research priorities. From December 2008 to February; the expert groups elaborated the first draft. The consultative process involved the active participation of many different countries. Consultation involved researchers, advisors, members of inspection/certification bodies, as well as different users/beneficiaries of the research such as farmers, processors, market actors and members of civil society organisations throughout Europe and further afield in order to gather the research needs of the whole organic sector

Unsupervised Context-Sensitive Spelling Correction of English and Dutch Clinical Free-Text with Word and Character N-Gram Embeddings

We present an unsupervised context-sensitive spelling correction method for clinical free-text that uses word and character n-gram embeddings. Our method generates misspelling replacement candidates and ranks them according to their semantic fit, by calculating a weighted cosine similarity between the vectorized representation of a candidate and the misspelling context. To tune the parameters of this model, we generate self-induced spelling error corpora. We perform our experiments for two languages. For English, we greatly outperform off-the-shelf spelling correction tools on a manually annotated MIMIC-III test set, and counter the frequency bias of a noisy channel model, showing that neural embeddings can be successfully exploited to improve upon the state-of-the-art. For Dutch, we also outperform an off-the-shelf spelling correction tool on manually annotated clinical records from the Antwerp University Hospital, but can offer no empirical evidence that our method counters the frequency bias of a noisy channel model in this case as well. However, both our context-sensitive model and our implementation of the noisy channel model obtain high scores on the test set, establishing a state-of-the-art for Dutch clinical spelling correction with the noisy channel model.Comment: Appears in volume 7 of the CLIN Journal, http://www.clinjournal.org/biblio/volum