Relevance Ranking of Intensive Care Nursing Narratives

Suominen, Hanna; Pahikkala, Tapio; Hiissa, Marketta; Lehtikunnas, Tuija; Back, Barbro; Karsten, Helena; Salanterä, Sanna; Salakoski, Tapio

doi:10.1007/11892960_87

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…We tailored these methods then into an unsupervised direction in order to meet the user's need for an ad hoc information search without fixing the set of possible search topics in advance. We described the methods in more detail in [32], [33], and [34] for sections 3.1.1, 3.1.2, and 3.1.3, respectively.…”

Section: Topical Searchmentioning

confidence: 99%

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Supporting Communication and Decision Making in Finnish Intensive Care with Language Technology

Suominen

Salakoski

2010

Journal of Healthcare Engineering

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A fluent flow of health information is critical for health communication and decision making. However, the flow is fragmented by the large amount of textual records and their specific jargon. This creates risks for both patient safety and cost-effective health services. Language technology for the automated processing of textual health records is emerging. In this paper, we describe method development for building topical overviews in Finnish intensive care. Our topical search methods are based on supervised multi-label classification and regression, as well as supervised and unsupervised multi-class classification. Our linguistic analysis methods are based on rulebased and statistical parsing, as well as tailoring of a commercial morphological analyser. According to our experimental results, the supervised methods generalise for multiple topics and human annotators, and the unsupervised method enables an ad hoc information search. Tailored linguistic analysis improves performance in the experiments and, in addition, improves text comprehensibility for health professionals and laypeople. In conclusion, the performance of our methods is promising for real-life applications.

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Section: Topical Searchmentioning

confidence: 99%

“…We supplemented the multi-label classification problem with the topical relevance evaluation in [33]. We trained our regression system with the combined expert annotation.…”

Section: Multi-label Regressionmentioning

confidence: 99%