See CLEF site 2015

See Conference paper

I collaborated in the CLEFeHealth2014 lab challemge. I created all the graphical elements: examples prototypes and publication figures.

2014-Conference-paper-CLEF2014wn-eHealth-SuominenEt2014

Discharge summary improved with text analysis and layout design

Discharge summary improved with text analysis and layout design

Hanna Suominen, Tobias Schreck, Gondy Leroy, Harry Hochheiser, Lorraine

Goeuriot, Liadh Kelly, Danielle L Mowery, Jaume Nualart, Gabriela Ferraro,

Daniel Keim

Status: published

Type: Conference Paper

Conference/location: CLEF 2014 Evaluation Labs and Workshop: Online Working Notes

Conference URL: http://clef2014.clef-initiative.eu/

ABSTRACT

Discharge summaries serve a variety of aims, ranging from clinical care to legal purposes. They are also important tools in patient empowerment, but a patient’s comprehension of the information is often suboptimal. Continu-ing in the tradition of focusing on automated approaches to increasing patient comprehension, The CLEFeHealth2014 lab tasked participants to visualize the information in discharge summaries while also providing connections to addi-tional online information. Participants were provided with six cases containing a discharge summary, patient profile and information needs. Of fifty registra-tions, only the FLPolytech team completed all requirements related to the task. They augmented the discharge summary by linking to external resources, insert-ing structure related to timing of the information need (past, present future), en-riching the content, i.e., with definitions, and providing meta-information, e.g., how to make future appointments. Four panellists evaluated the submission. Overall, they were positive about the enhancements, but all agreed that addi-tional visualization could further improve the provided solution. Contributor Statement: HS, TS, GL, HSH, DK, LG, and LK designed the task and its evaluation methodology. Together with JN and GF, they developed the task description. LG and LK chose the six patient cases and extracted the re-spective subset from the CLEFeHealth2013 data. HS and DLM automatically de-identified discharge summaries of this subset by hand. HS, HH, TS, JN, and 1

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