IMIA Yearbook of Medical Informatics 2013 1 Knowledge Representation and Management: Towards an Integration of a Semantic Web in Daily Health Practice Knowledge Representation and Management: Towards an Integration of a Semantic Web in Daily Health Practi

Summary Objective: To summarize the best papers in the field of Knowledge Representation and Management (KRM). Methods: A synopsis of the four selected articles for the IMIA Yearbook 2013 KRM section is provided, as well as highlights of current KRM trends, in particular, of the semantic web in daily health practice. The manual selection was performed in three stages: first a set of 3,106 articles, then a second set of 86 articles followed by a third set of 15 articles, and finally the last set of four chosen articles. Results: Among the four selected articles (se

Decision Support for Intoxication Prediction Using Graph Convolutional Networks

Every day, poison control centers (PCC) are called for immediate classification and treatment recommendations if an acute intoxication is suspected. Due to the time-sensitive nature of these cases, doctors are required to propose a correct diagnosis and intervention within a minimal time frame. Usually the toxin is known and recommendations can be made accordingly. However, in challenging cases only symptoms are mentioned and doctors have to rely on their clinical experience. Medical experts and our analyses of a regional dataset of intoxication records provide evidence that this is challenging, since occurring symptoms may not always match the textbook description due to regional distinctions, inter-rater variance, and institutional workflow. Computer-aided diagnosis (CADx) can provide decision support, but approaches so far do not consider additional information of the reported cases like age or gender, despite their potential value towards a correct diagnosis. In this work, we propose a new machine learning based CADx method which fuses symptoms and meta information of the patients using graph convolutional networks. We further propose a novel symptom matching method that allows the effective incorporation of prior knowledge into the learning process and evidently stabilizes the poison prediction. We validate our method against 10 medical doctors with different experience diagnosing intoxication cases for 10 different toxins from the PCC in Munich and show our method's superiority in performance for poison prediction.Comment: 10 pages, 3 figure

A time predefined variable depth search for nurse rostering

This paper presents a variable depth search for the nurse rostering problem. The algorithm works by chaining together single neighbourhood swaps into more effective compound moves. It achieves this by using heuristics to decide whether to continue extending a chain and which candidates to examine as the next potential link in the chain. Because end users vary in how long they are willing to wait for solutions, a particular goal of this research was to create an algorithm that accepts a user specified computational time limit and uses it effectively. When compared against previously published approaches the results show that the algorithm is very competitive

Mapping the history and current situation of research on John Cunningham virus – a bibliometric analysis

<p>Abstract</p> <p>Background</p> <p>John Cunningham virus (JCV) constitutes a family of polyoma viruses, which plays important roles in the progressive multifocal leukoencephalopathy (PML) and tumorigenesis. However, no bibliometric investigation has been reported to guide the researchers and potential readers.</p> <p>Methods</p> <p>Papers were collected from database Sci-expanded and Pubmed until May 22, 2008. The highly-productive authors, institutes and countries, highly-cited authors and journals were ranked. The highly-cited articles were subjected to co-citation and chronological analysis with highly-frequent MeSH words for co-occurrence analysis.</p> <p>Results</p> <p>Until now, 1785 articles about JCV were indexed in Sci-expanded and 1506 in Pubmed. The main document type was original article. USA, Japan and Italy were the largest three producers about JCV. Temple University published 128 papers and ranked the top, followed by University of Tokyo. Khalili K and Yogo Y became the core authors due to more than 20 documents produced. Journal of Neurovirology published more than 15 papers and ranked the top. Padgett BL and Berger JR were the first two highly-cited authors. Journal of Virology and Journal of Neurovirology respectively ranked to the first two highly-cited journals. These top highly-cited articles were divided into 5 aspects: (1) The correlation between JC virus and tumors; (2) Causal correlation of JCV with PML; (3) Polyoma virus infection and its related diseases in renal-allograft recipients; (4) Detection of JCV antibody, oncogene and its encoding protein; (5) Genetics and molecular biology of JCV. The MeSH/subheadings were classified into five groups: (1) JCV and virus infectious diseases; (2) JCV pathogenicity and pathological appearance of PML; (3) JCV isolation and detection; (4) Immunology of JCV and PML; (5) JCV genetics and tumors.</p> <p>Conclusion</p> <p>JCV investigation mainly focused on its isolation and detection, as well as its correlation with PML and tumors. Establishment of transgenic animal model using JCV T antigen would be a hopeful and useful project in the further study.</p

Knowledge-Based Query Expansion over a Medical Terminology Oriented Ontology

Abstract. This paper deals with the problem of information retrieval on the Web and present the CISMeF project (acronym of Catalogue and Index of French-speaking Medical Sites). Information retrieval in the CISMeF catalogue is done with a terminology that is similar to ontology of medical domain and a set of metadata. This allows us to place the project at an overlap between the present Web, which is informal, and the forthcoming Semantic Web. We also describe an ongoing work, which consists of applying three knowledge-based methods in order to enhance information retrieval