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This paper describes the participation of the SNUMedinfo team at the CLEFeHealth2014 task 3. We submitted 7 runs to Task3a (monolingual information retrieval): 1 baseline run using query likelihood model in Indri search engine; 3 runs applying UMLS based lexical query expansion utilizing discharge summary as an expansion term filter; 3 runs applying(More)
Healthcare information travels with patients and clinicians and therefore the need for information to be ubiquitously available is key to reliable patient care and reliable medical systems. We have implemented MobileNurse, a prototype point-of-care system using PDA. MobileNurse has four modules each of which performs: (1) patient information management; (2)(More)
This paper proposes a novel framework for up-conversion of depth video resolution both in spatial and in time domain. Time-of-Flight (TOF) sensors are widely used in computer vision fields. Although TOF sensors provide depth video in real time, there are some problems in a sense that it provides a low resolution and a low frame-rate depth video. We propose(More)
Visualizing narrative medical events into a timeline can have positive effects on clinical environments. However, the characteristics of natural language and medical environments make this representation more difficult. This paper explains the obstacles and suggests a solution called the V-Model. The V-Model is a new innovative time model that was developed(More)
OBJECTIVE In medical information retrieval research, semantic resources have been mostly used by expanding the original query terms or estimating the concept importance weight. However, implicit term-dependency information contained in semantic concept terms has been overlooked or at least underused in most previous studies. In this study, we incorporate a(More)
Patient clinical data are distributed and often fragmented in heterogeneous systems, and therefore the need for information integration is a key to reliable patient care. Once the patient data are orderly integrated and readily available, the problems in accessing the distributed patient clinical data, the well-known difficulties of adopting a mobile health(More)
This paper describes the participation of the SNUMedinfo team at the BioASQ Task 2a and Task 2b of CLEF 2014 Question Answering track. Task 2a was about biomedical semantic indexing. We trained SVM classifiers to automatically assign relevant MeSH descriptors to the MEDLINE article. Regarding Task 2b biomedical question answering, we participated at the(More)
OBJECTIVE To determine whether SVM-based classifiers, which are trained on a combination of inclusion and common exclusion articles, are useful to experts reviewing journal articles for inclusion during new systematic reviews. METHODS Test collections were built using the annotated reference files from 19 procedure and 4 drug systematic reviews. The(More)