Entity

Time filter

Source Type


De Bruin J.S.,Medical University of Vienna | Blacky A.,Medical University of Vienna | Koller W.,Medical University of Vienna | Adlassnig K.-P.,Medical University of Vienna | Adlassnig K.-P.,Medexter Healthcare GmbH
Studies in Health Technology and Informatics | Year: 2013

Central venous catheters play an important role in patient care in intensive care units (ICUs), but their use comes at the risk of catheter-related infections (CRIs). Electronic surveillance systems can detect CRIs more accurately than manual surveillance, but these systems often omit patients that do not exhibit all infection signs to their full degree, the so-called borderline group. By extending an electronic surveillance system with fuzzy constructs, the borderline group can be identified. In this study, we examined the size of the borderline group for systemic CRIs (CRI2) by calculating the frequency of fuzzy values for CRI2 and related infection parameters in patient data involving ten ICUs (75 beds) over one year. We also validated the expert-defined fuzzy constructs by comparing overall and CRI2-specific support. The study showed that more than 86% of the data contained fuzzy values, and that the borderline group for CRI2 consisted of 2% of the study group. It was also confirmed that most fuzzy constructs were good representatives of the borderline CRI2 patient group. © 2013 IMIA and IOS Press. Source


Samwald M.,Medical University of Vienna | Gimenez J.A.M.,Medical University of Vienna | Gimenez J.A.M.,Medical University of Graz | Boyce R.D.,University of Pittsburgh | And 4 more authors.
BMC Medical Informatics and Decision Making | Year: 2015

Background: Every year, hundreds of thousands of patients experience treatment failure or adverse drug reactions (ADRs), many of which could be prevented by pharmacogenomic testing. However, the primary knowledge needed for clinical pharmacogenomics is currently dispersed over disparate data structures and captured in unstructured or semi-structured formalizations. This is a source of potential ambiguity and complexity, making it difficult to create reliable information technology systems for enabling clinical pharmacogenomics. Methods: We developed Web Ontology Language (OWL) ontologies and automated reasoning methodologies to meet the following goals: 1) provide a simple and concise formalism for representing pharmacogenomic knowledge, 2) finde errors and insufficient definitions in pharmacogenomic knowledge bases, 3) automatically assign alleles and phenotypes to patients, 4) match patients to clinically appropriate pharmacogenomic guidelines and clinical decision support messages and 5) facilitate the detection of inconsistencies and overlaps between pharmacogenomic treatment guidelines from different sources. We evaluated different reasoning systems and test our approach with a large collection of publicly available genetic profiles. Results: Our methodology proved to be a novel and useful choice for representing, analyzing and using pharmacogenomic data. The Genomic Clinical Decision Support (Genomic CDS) ontology represents 336 SNPs with 707 variants; 665 haplotypes related to 43 genes; 22 rules related to drug-response phenotypes; and 308 clinical decision support rules. OWL reasoning identified CDS rules with overlapping target populations but differing treatment recommendations. Only a modest number of clinical decision support rules were triggered for a collection of 943 public genetic profiles. We found significant performance differences across available OWL reasoners. Conclusions: The ontology-based framework we developed can be used to represent, organize and reason over the growing wealth of pharmacogenomic knowledge, as well as to identify errors, inconsistencies and insufficient definitions in source data sets or individual patient data. Our study highlights both advantages and potential practical issues with such an ontology-based approach. © 2015 Samwald et al.; licensee BioMed Central. Source


Samwald Dr. M.,Medical University of Vienna | Samwald Dr. M.,Vienna University of Technology | Adlassnig K.-P.,Medical University of Vienna | Adlassnig K.-P.,Medexter Healthcare GmbH
Journal of the American Medical Informatics Association | Year: 2013

A sizable fraction of patients experiences adverse drug events or lack of drug efficacy. A part of this variability in drug response can be explained by genetic differences between patients. However, pharmacogenomic data as well as computational clinical decision support systems for interpreting such data are still unavailable in most healthcare settings. We address this problem by introducing the medicine safety code (MSC), which captures compressed pharmacogenomic data in a twodimensional barcode that can be carried in a patient's wallet. We successfully encoded data about 385 genetic polymorphisms in MSC and were able to decode and interpret MSC quickly with common mobile devices. The MSC could make individual pharmacogenomic data and decision support available in a wide variety of healthcare settings without the set up of large-scale infrastructures or centralized databases. Source


Minarro-Gimenez J.A.,Medical University of Vienna | Blagec K.,Medical University of Vienna | Boyce R.D.,University of Pittsburgh | Adlassnig K.-P.,Medical University of Vienna | And 2 more authors.
PLoS ONE | Year: 2014

Background: The development of genotyping and genetic sequencing techniques and their evolution towards low costs and quick turnaround have encouraged a wide range of applications. One of the most promising applications is pharmacogenomics, where genetic profiles are used to predict the most suitable drugs and drug dosages for the individual patient. This approach aims to ensure appropriate medical treatment and avoid, or properly manage, undesired side effects. Results: We developed the Medicine Safety Code (MSC) service, a novel pharmacogenomics decision support system, to provide physicians and patients with the ability to represent pharmacogenomic data in computable form and to provide pharmacogenomic guidance at the point-of-care. Pharmacogenomic data of individual patients are encoded as Quick Response (QR) codes and can be decoded and interpreted with common mobile devices without requiring a centralized repository for storing genetic patient data. In this paper, we present the first fully functional release of this system and describe its architecture, which utilizes Web Ontology Language 2 (OWL 2) ontologies to formalize pharmacogenomic knowledge and to provide clinical decision support functionalities. Conclusions: The MSC system provides a novel approach for enabling the implementation of personalized medicine in clinical routine. © 2014 Miñarro-Giménez et al. Source


De Bruin J.S.,Medical University of Vienna | Blacky A.,Vienna University Hospital | Adlassnig K.-P.,Medical University of Vienna | Adlassnig K.-P.,Medexter Healthcare GmbH
Studies in Health Technology and Informatics | Year: 2012

Central venous catheters (CVCs) play an essential role in the care of the critically ill, but their use comes at the risk of infection. By using fuzzy set theory and logic to model clinical linguistic CVC-related infection criteria, clinical detection systems can detect borderline infections where not all infection parameters have been (fully) met, also called fuzzy results. In this paper we analyzed the clinical use of these results. We used a fuzzy-logic-based computerized infection control system for the monitoring of healthcare-associated infections to uncover fuzzy results and periods, after which we classified them, and used these classifications together with knowledge of prior CVC-related infection episodes in temporal association rule mining. As a result, we uncovered several rules which can help with the early detection of re-occurring CVC-related infections. © 2012 European Federation for Medical Informatics and IOS Press. All rights reserved. Source

Discover hidden collaborations