Peute, Linda W. P.de Keizer, Nicolette F.Jaspers, Monique W. M.
10页
查看更多>>摘要：Objective: To compare the performance of the Concurrent (CFA) and Retrospective (RTA) Think Aloud method and to assess their value in a formative usability evaluation of an Intensive Care Registry-physician data query tool designed to support ICU quality improvement processes.
查看更多>>摘要：Background: Extensive deployment and sustainability of integrated care services (ICS) constitute an unmet need to reduce the burden of chronic conditions. The European Union project NEXES (20082013) assessed the deployment of four ICS encompassing the spectrum of severity of chronic patients.
Wilbur, W. JohnKim, SunLiu, HaibinYeganova, Lana...
8页
查看更多>>摘要：Identifying unknown drug interactions is of great benefit in the early detection of adverse drug reactions. Despite existence of several resources for drug-drug interaction (DDI) information, the wealth of such information is buried in a body of unstructured medical text which is growing exponentially. This calls for developing text mining techniques for identifying DDIs. The state-of-the-art DDI extraction methods use Support Vector Machines (SVMs) with non-linear composite kernels to explore diverse contexts in literature. While computationally less expensive, linear kernel-based systems have not achieved a comparable performance in DDI extraction tasks. In this work, we propose an efficient and scalable system using a linear kernel to identify DDI information. The proposed approach consists of two steps: identifying DDIs and assigning one of four different DDI types to the predicted drug pairs. We demonstrate that when equipped with a rich set of lexical and syntactic features, a linear SVM classifier is able to achieve a competitive performance in detecting DDIs. In addition, the one-against-one strategy proves vital for addressing an imbalance issue in DDI type classification. Applied to the DDIExtraction 2013 corpus, our system achieves an F1 score of 0.670, as compared to 0.651 and 0.609 reported by the top two participating teams in the DDIExtraction 2013 challenge, both based on non-linear kernel methods. Published by Elsevier Inc.
查看更多>>摘要：It is widely believed that Electronic Health Records (EHR) improve medical decision-making by enabling medical staff to access medical information stored in the system. It remains unclear, however, whether EHR indeed fulfills this claim under the severe time constraints of Emergency Departments (EDs). We assessed whether accessing EHR in an ED actually improves decision-making by clinicians. A simulated ED environment was created at the Israel Center for Medical Simulation (MSR). Four different actors were trained to simulate four specific complaints and behavior and 'consulted' 26 volunteer ED physicians. Each physician treated half of the cases (randomly) with access to EHR, and their medical decisions were compared to those where the physicians had no access to EHR. Comparison of diagnostic accuracy with and without access showed that accessing the EHR led to an increase in the quality of the clinical decisions. Physicians accessing EHR were more highly informed and thus made more accurate decisions. The percentage of correct diagnoses was higher and these physicians were more confident in their diagnoses and made their decisions faster. (C) 2015 Elsevier Inc. All rights reserved.
查看更多>>摘要：With the rapid development of information technologies, tremendous amount of data became readily available in various application domains. This big data era presents challenges to many conventional data analytics research directions including data capture, storage, search, sharing, analysis, and visualization. It is no surprise to see that the success of next-generation healthcare systems heavily relies on the effective utilization of gigantic amounts of medical data. The ability of analyzing big data in modern healthcare systems plays a vital role in the improvement of the quality of care delivery.
Sousa, Ana MargaridaOlivia Pereira, MariaLourenco, Analia
9页
查看更多>>摘要：Background: One of the major concerns of the biomedical community is the increasing prevalence of antimicrobial resistant microorganisms. Recent findings show that the diversification of colony morphology may be indicative of the expression of virulence factors and increased resistance to antibiotic therapeutics. To transform these findings, and upcoming results, into a valuable clinical decision making tool, colony morphology characterisation should be standardised. Notably, it is important to establish the minimum experimental information necessary to contextualise the environment that originated the colony morphology, and describe the main morphological features associated unambiguously.
查看更多>>摘要：Targeted anticancer drugs such as imatinib, trastuzumab and erlotinib dramatically improved treatment outcomes in cancer patients, however, these innovative agents are often associated with unexpected side effects. The pathophysiological mechanisms underlying these side effects are not well understood. The availability of a comprehensive knowledge base of side effects associated with targeted anticancer drugs has the potential to illuminate complex pathways underlying toxicities induced by these innovative drugs. While side effect association knowledge for targeted drugs exists in multiple heterogeneous data sources, published full-text oncological articles represent an important source of pivotal, investigational, and even failed trials in a variety of patient populations. In this study, we present an automatic process to extract targeted anticancer drug-associated side effects (drug-SE pairs) from a large number of high profile full-text oncological articles.
查看更多>>摘要：CSIRO Adverse Drug Event Corpus (CADEc) is a new rich annotated corpus of medical forum posts on patient-reported Adverse Drug Events (ADEs). The corpus is sourced from posts on social media, and contains text that is largely written in colloquial language and often deviates from formal English grammar and punctuation rules. Annotations contain mentions of concepts such as drugs, adverse effects, symptoms, and diseases linked to their corresponding concepts in controlled vocabularies, i.e., SNOMED Clinical Terms and MedDRA. The quality of the annotations is ensured by annotation guidelines, multi-stage annotations, measuring inter-annotator agreement, and final review of the annotations by a clinical terminologist. This corpus is useful for studies in the area of information extraction, or more generally text mining, from social media to detect possible adverse drug reactions from direct patient reports. The corpus is publicly available at https://data.csiro.au.(1) (C) 2015 Elsevier Inc. All rights reserved.
Chen, YouGhosh, JoydeepBejan, Cosmin AdrianGunter, Carl A....
12页
查看更多>>摘要：Objective: Data in electronic health records (EHRs) is being increasingly leveraged for secondary uses, ranging from biomedical association studies to comparative effectiveness. To perform studies at scale and transfer knowledge from one institution to another in a meaningful way, we need to harmonize the phenotypes in such systems. Traditionally, this has been accomplished through expert specification of phenotypes via standardized terminologies, such as billing codes. However, this approach may be biased by the experience and expectations of the experts, as well as the vocabulary used to describe such patients. The goal of this work is to develop a data-driven strategy to (1) infer phenotypic topics within patient populations and (2) assess the degree to which such topics facilitate a mapping across populations in disparate healthcare systems.
Gardeux, VincentBosco, AnthonyLi, JianrongHalonen, Marilyn J....
10页
查看更多>>摘要：Background: Understanding individual patient host-response to viruses is key to designing optimal personalized therapy. Unsurprisingly, in vivo human experimentation to understand individualized dynamic response of the transcriptome to viruses are rarely studied because of the obvious limitations stemming from ethical considerations of the clinical risk.
NSTL
Elsevier