查看更多>>摘要：Purpose: Older adults are at increased risk of adverse drug events due to medication. Older adults tend to take more medication and are at higher risk of chronic illness. Over-the-counter (OTC) medication does not require healthcare provider oversight and understanding OTC information is heavily dependent on a consumer's ability to understand and use the medication appropriately. Coupling health technology with effective communication is one approach to address the challenge of communicating health and improving health related tasks. However, the success of many health technologies also depends on how well the technology is designed and how well it addresses users needs. This is especially true for the older adult population. This paper describes (1) a formative study performed to understand how to design novel health technology to assist older adults with OTC medication information, and (2) how a user-centered design process helped to refine the initial assumptions of user needs and help to conceptualize the technology.
查看更多>>摘要：Objective: To improve neonatal patient safety through automated detection of medication administration errors (MAEs) in high alert medications including narcotics, vasoactive medication, intravenous fluids, parenteral nutrition, and insulin using the electronic health record (EHR); to evaluate rates of MAEs in neonatal care; and to compare the performance of computerized algorithms to traditional incident reporting for error detection.
查看更多>>摘要：Databases of curated biomedical knowledge, such as the protein-locations reflected in the UniProtKB database, provide an accurate and useful resource to researchers and decision makers. Our goal is to augment the manual efforts currently used to curate knowledge bases with automated approaches that leverage the increased availability of full-text scientific articles. This paper describes experiments that use distant supervised learning to identify protein subcellular localizations, which are important to understand protein function and to identify candidate drug targets. Experiments consider Swiss-Prot, the manually annotated subset of the UniProtKB protein knowledge base, and 43,000 full-text articles from the Journal of Biological Chemistry that contain just under 11.5 million sentences. The system achieves 0.81 precision and 0.49 recall at sentence level and an accuracy of 57% on held-out instances in a test set. Moreover, the approach identifies 8210 instances that are not in the UniProtKB knowledge base. Manual inspection of the 50 most likely relations showed that 41(82%) were valid. These results have immediate benefit to researchers interested in protein function, and suggest that distant supervision should be explored to complement other manual data curation efforts. (C) 2015 Elsevier Inc. All rights reserved.
查看更多>>摘要：Objective: Some phase 1 clinical trials offer strong financial incentives for healthy individuals to participate in their studies. There is evidence that some individuals enroll in multiple trials concurrently. This creates safety risks and introduces data quality problems into the trials. Our objective was to construct a privacy preserving protocol to track phase 1 participants to detect concurrent enrollment.
查看更多>>摘要：Biclustering has become a popular technique for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. Most of biclustering approaches use a measure or cost function that determines the quality of biclusters. In such cases, the development of both a suitable heuristics and a good measure for guiding the search are essential for discovering interesting biclusters in an expression matrix. Nevertheless, not all existing biclustering approaches base their search on evaluation measures for biclusters. There exists a diverse set of biclustering tools that follow different strategies and algorithmic concepts which guide the search towards meaningful results. In this paper we present a extensive survey of biclustering approaches, classifying them into two categories according to whether or not use evaluation metrics within the search method: biclustering algorithms based on evaluation measures and non metric-based biclustering algorithms. In both cases, they have been classified according to the type of meta-heuristics which they are based on. (C) 2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license
查看更多>>摘要：Pandemic influenza is a major concern worldwide. Availability of advanced technologies and the nucleotide sequences of a large number of pandemic and non-pandemic influenza viruses in 2009 provide a great opportunity to investigate the underlying rules of pandemic induction through data mining tools. Here, for the first time, an integrated classification and association rule mining algorithm (CBA) was used to discover the rules underpinning alteration of non-pandemic sequences to pandemic ones. We hypothesized that the extracted rules can lead to the development of an efficient expert system for prediction of influenza pandemics. To this end, we used a large dataset containing 5373 HA (hemagglutinin) segments of the 2009 HI NI pandemic and non-pandemic influenza sequences. The analysis was carried out for both nucleotide and protein sequences. We found a number of new rules which potentially present the undiscovered antigenic sites at influenza structure. At the nucleotide level, alteration of thymine (T) at position 260 was the key discriminating feature in distinguishing non-pandemic from pandemic sequences. At the protein level, rules including I233K, M334L were the differentiating features. CBA efficiently classifies pandemic and non-pandemic sequences with high accuracy at both the nucleotide and protein level. Finding hotspots in influenza sequences is a significant finding as they represent the regions with low antibody reactivity. We argue that the virus breaks host immunity response by mutation at these spots. Based on the discovered rules, we developed the software, "Prediction of Pandemic Influenza" for discrimination of pandemic from non-pandemic sequences. This study opens a new vista in discovery of association rules between mutation points during evolution of pandemic influenza. (C) 2015 Elsevier Inc. All rights reserved.
查看更多>>摘要：In a number of biological studies, the raw gene expression data are not usually published due to different causes, such as data privacy and patent rights. Instead, significant gene lists with fold change values are usually provided in most studies. However, due to variations in data sources and profiling conditions, only a small number of common significant genes could be found among similar studies. Moreover, traditional gene set based analyses that consider these genes have not taken into account the fold change values, which may be important to distinguish between the different levels of significance of the genes. Human embryonic stem cell derived cardiomyocytes (hESC-CM) is a good representative of this category. hESC-CMs, with its role as a potentially unlimited source of human heart cells for regenerative medicine, have attracted the attentions of biological and medical researchers. Because of the difficulty of acquiring data and the resulting expenses, there are only a few related hESC-CM studies and few hESC-CM gene expression data are provided. In view of these challenges, we propose a new Gene Set Enrichment Ensemble (GSEE) approach to perform gene set based analysis on individual studies based on significant up-regulated gene lists with fold change data only. Our approach provides both explicit and implicit ways to utilize the fold change data, in order to make full use of scarce data. We validate our approach with hESC-CM data and fetal heart data, respectively. Experimental results on significant gene lists from different studies illustrate the effectiveness of our proposed approach. (C) 2015 Elsevier Inc. All rights reserved.
查看更多>>摘要：Motivation: Although full-text articles are provided by the publishers in electronic formats, it remains a challenge to find related work beyond the title and abstract context. Identifying related articles based on their abstract is indeed a good starting point; this process is straightforward and does not consume as many resources as full-text based similarity would require. However, further analyses may require in-depth understanding of the full content. Two articles with highly related abstracts can be substantially different regarding the full content. How similarity differs when considering title-and-abstract versus full-text and which semantic similarity metric provides better results when dealing with full-text articles are the main issues addressed in this manuscript.
查看更多>>摘要：Electronic Health Records (EHRs) present the opportunity to observe serial measurements on patients. While potentially informative, analyzing these data can be challenging. In this work we present a means to classify individuals based on a series of measurements collected by an EHR. Using patients undergoing hemodialysis, we categorized people based on their intradialytic blood pressure. Our primary criteria were that the classifications were time dependent and independent of other subjects. We fit a curve of intradialytic blood pressure using regression splines and then calculated first and second derivatives to come up with four mutually exclusive classifications at different time points. We show that these classifications relate to near term risk of cardiac events and are moderately stable over a succeeding two-week period. This work has general application for analyzing dense EHR data. (C) 2015 Elsevier Inc. All rights reserved.
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