Louhi 2010: Special issue on Text and Data Mining of Health Documents.

The papers presented in this supplement focus and reflect on computer use in every-day clinical work in hospitals and clinics such as electronic health record systems, pre-processing for computer aided summaries, clinical coding, computer decision systems, as well as related ethical concerns and security. Much of this work concerns itself by necessity with incorporation and development of language processing tools and methods, and as such this supplement aims at providing an arena for reporting on development in a diversity of languages. In the supplement we can read about some of the challenges identified above.

Characteristics of Finnish and Swedish intensive care nursing narratives: a comparative analysis to support the development of clinical language technologies.

BACKGROUND: Free 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. METHODS: Our 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. RESULTS: Although 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. CONCLUSIONS: The 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. AVAILABILITY: The Finnish and Swedish data can be found at: http://www.dsv.su.se/hexanord/data/.

Underreporting of maritime accidents to vessel accident databases.

Underreporting of maritime accidents is a problem not only for authorities trying to improve maritime safety through legislation, but also to risk management companies and other entities using maritime casualty statistics in risk and accident analysis. This study collected and compared casualty data from 01.01.2005 to 31.12.2009, from IHS Fairplay and the maritime authorities from a set of nations. The data was compared to find common records, and estimation of the true number of occurred accidents was performed using conditional probability given positive dependency between data sources, several variations of the capture-recapture method, calculation of best case scenario assuming perfect reporting, and scaling up a subset of casualty information from a marine insurance statistics database. The estimated upper limit reporting performance for the selected flag states ranged from 14% to 74%, while the corresponding estimated coverage of IHS Fairplay ranges from 4% to 62%. On average the study results document that the number of unreported accidents makes up roughly 50% of all occurred accidents. Even in a best case scenario, only a few flag states come close to perfect reporting (94%). The considerable scope of underreporting uncovered in the study, indicates that users of statistical vessel accident data should assume a certain degree of underreporting, and adjust their analyses accordingly. Whether to use correction factors, a safety margin, or rely on expert judgment, should be decided on a case by case basis.

Developing a standard for de-identifying electronic patient records written in Swedish: precision, recall and F-measure in a manual and computerized annotation trial.

BACKGROUND: Electronic patient records (EPRs) contain a large amount of information written in free text. This information is considered very valuable for research but is also very sensitive since the free text parts may contain information that could reveal the identity of a patient. Therefore, methods for de-identifying EPRs are needed. The work presented here aims to perform a manual and automatic Protected Health Information (PHI)-annotation trial for EPRs written in Swedish. METHODS: This study consists of two main parts: the initial creation of a manually PHI-annotated gold standard, and the porting and evaluation of an existing de-identification software written for American English to Swedish in a preliminary automatic de-identification trial. Results are measured with precision, recall and F-measure. RESULTS: This study reports fairly high Inter-Annotator Agreement (IAA) results on the manually created gold standard, especially for specific tags such as names. The average IAA over all tags was 0.65 F-measure (0.84 F-measure highest pairwise agreement). For name tags the average IAA was 0.80 F-measure (0.91 F-measure highest pairwise agreement). Porting a de-identification software written for American English to Swedish directly was unfortunately non-trivial, yielding poor results. CONCLUSION: Developing gold standard sets as well as automatic systems for de-identification tasks in Swedish is feasible. However, discussions and definitions on identifiable information is needed, as well as further developments both on the tag sets and the annotation guidelines, in order to get a reliable gold standard. A completely new de-identification software needs to be developed.