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BackgroundFree 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.MethodsOur 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.ResultsAlthough 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.ConclusionsThe 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.AvailabilityThe Finnish and Swedish data can be found at: http://www.dsv.su.se/hexanord/data/.

The Norwegian Petroleum Safety Authority (PSA) requires offshore petroleum operators on the Norwegian Continental Shelf (NCS) to perform risk assessments of impacts (allisions) between passing ships and offshore installations. These risk assessments provide a basis for defining the allision accidental load that the installation shall be designed for. Even though the risk of allision is small, the potential consequences can be catastrophic. In a worst-case scenario, an allision may result in the total loss of an installation. The ageing industry standard allision risk model, COLLIDE, calculates the risk of impacts between passing (non-field-related) ships and installations based on Automatic Identification System (AIS) data. Both the COLLIDE risk model and a new Bayesian allision risk model currently under development are highly sensitive to variations in vessels' passing distances, especially close proximity passings. Allision risk assessments are typically performed during the design and development phase of an installation, which means that historical AIS data are used Bas is^, disregarding future changes to the traffic pattern when the new installation is placed on a location. This article presents an empirical study of one of the most important variables used to calculate the risk of allision from passing vessels, namely passing distance. The study shows that merchant vessels alter course to achieve a safe passing distance to new surface offshore petroleum installations. This indicates that the results of current allision risk assessments are overly conservative.

Abstract. The prediction of diagnosis codes is typically based on freetext entries in clinical documents. Previous attempts to tackle this problem range from strictly rule-based systems to utilizing various classification algorithms, resulting in varying degrees of success. A novel approach is to build a word space model based on a corpus of coded patient records, associating co-occurrences of words and ICD-10 codes. Random Indexing is a computationally efficient implementation of the word space model and may prove an effective means of providing support for the assignment of diagnosis codes. The method is here qualitatively evaluated for its feasibility by a physician on clinical records from two Swedish clinics. The assigned codes were in this initial experiment found among the top 10 generated suggestions in 20% of the cases, but a partial match in 77% demonstrates the potential of the method.