Akgun, MeteBayrak, A. OsmanOzer, BugraSagiroglu, M. Samil...
9页
查看更多>>摘要：Recently, the rapid advance in genome sequencing technology has led to production of huge amount of sensitive genomic data. However, a serious privacy challenge is confronted with increasing number of genetic tests as genomic data is the ultimate source of identity for humans. Lately, privacy threats and possible solutions regarding the undesired access to genomic data are discussed, however it is challenging to apply proposed solutions to real life problems due to the complex nature of security definitions. In this review, we have categorized pre-existing problems and corresponding solutions in more understandable and convenient way. Additionally, we have also included open privacy problems coming with each genomic data processing procedure. We believe our classification of genome associated privacy problems will pave the way for linking of real-life problems with previously proposed methods. (C) 2015 Elsevier Inc. All rights reserved.
Andonegui, JoseEguzkiza, AitorTrigo, Jesus DanielMartinez-Espronceda, Miguel...
15页
查看更多>>摘要：Most healthcare services use information and communication technologies to reduce and redistribute the workload associated with follow-up of chronic conditions. However, the lack of normalization of the information handled in and exchanged between such services hinders the scalability and extendibility. The use of medical standards for modelling and exchanging information, especially dual-model based approaches, can enhance the features of screening services. Hence, the approach of this paper is twofold. First, this article presents a generic methodology to model patient-centered clinical processes. Second, a proof of concept of the proposed methodology was conducted within the diabetic retinopathy (DR) screening service of the Health Service of Navarre (Spain) in compliance with a specific dual-model norm (openEHR). As a result, a set of elements required for deploying a model-driven DR screening service has been established, namely: clinical concepts, archetypes, termsets, templates, guideline definition rules, and user interface definitions. This model fosters reusability, because those elements are available to be downloaded and integrated in any healthcare service, and interoperability, since from then on such services can share information seamlessly. (C) 2015 Elsevier Inc. All rights reserved.
查看更多>>摘要：Biomedical ontologies are a critical component in biomedical research and practice. As an ontology evolves, its structure and content change in response to additions, deletions and updates. When editing a biomedical ontology, small local updates may affect large portions of the ontology, leading to unintended and potentially erroneous changes. Such unwanted side effects often go unnoticed since biomedical ontologies are large and complex knowledge structures. Abstraction networks, which provide compact summaries of an ontology's content and structure, have been used to uncover structural irregularities, inconsistencies and errors in ontologies. In this paper, we introduce Diff Abstraction Networks ("Diff AbNs"), compact networks that summarize and visualize global structural changes due to ontology editing operations that result in a new ontology release. A Diff AbN can be used to support curators in identifying unintended and unwanted ontology changes. The derivation of two Diff AbNs, the Diff Area Taxonomy and the Diff Partial-area Taxonomy, is explained and Diff Partial-area Taxonomies are derived and analyzed for the Ontology of Clinical Research, Sleep Domain Ontology, and eagle-i Research Resource Ontology. Diff Taxonomy usage for identifying unintended erroneous consequences of quality assurance and ontology merging are demonstrated. (C) 2015 Elsevier Inc. All rights reserved.
Lawson, Karla A.Duzinski, Sarah V.Vikalo, HansArzeno, Natalia M....
12页
查看更多>>摘要：Many in-hospital mortality risk prediction scores dichotomize predictive variables to simplify the score calculation. However, hard thresholding in these additive stepwise scores of the form "add chi points if variable upsilon is above/below threshold t" may lead to critical failures. In this paper, we seek to develop risk prediction scores that preserve clinical knowledge embedded in features and structure of the existing additive stepwise scores while addressing limitations caused by variable dichotomization. To this end, we propose a novel score structure that relies on a transformation of predictive variables by means of nonlinear logistic functions facilitating smooth differentiation between critical and normal values of the variables. We develop an optimization framework for inferring parameters of the logistic functions for a given patient population via cyclic block coordinate descent. The parameters may readily be updated as the patient population and standards of care evolve. We tested the proposed methodology on two populations: (I) brain trauma patients admitted to the intensive care unit of the Dell Children's Medical Center of Central Texas between 2007 and 2012, and (2) adult ICU patient data from the MIMIC II database. The results are compared with those obtained by the widely used PRISM III and SOFA scores. The prediction power of a score is evaluated using area under ROC curve, Youden's index, and precision-recall balance in a cross-validation study. The results demonstrate that the new framework enables significant performance improvements over PRISM III and SOFA in terms of all three criteria. (C) 2015 Elsevier Inc. All rights reserved.
Hamed, Ahmed AbdeenWu, XindongErickson, RobertFandy, Tamer...
12页
查看更多>>摘要：The importance of searching biomedical literature for drug interaction and side-effects is apparent. Current digital libraries (e.g., PubMed) suffer infrequent tagging and metadata annotation updates. Such limitations cause absence of linking literature to new scientific evidence. This demonstrates a great deal of challenges that stand in the way of scientists when searching biomedical repositories. In this paper, we present a network mining approach that provides a bridge for linking and searching drug-related literature. Our contributions here are two fold: (1) an efficient algorithm called HashPairMiner to address the run-time complexity issues demonstrated in its predecessor algorithm: HashnetMiner, and (2) a database of discoveries hosted on the web to facilitate literature search using the results produced by HashPairMiner. Though the K-H network model and the HashPairMiner algorithm are fairly young, their outcome is evidence of the considerable promise they offer to the biomedical science community in general and the drug research community in particular. (C) 2015 Elsevier Inc. All rights reserved.
查看更多>>摘要：Introduction: Investments of resources to purposively improve the movement of information between health system providers are currently made with imperfect information. No inventories of system -level electronic health information flows currently exist, nor do measures of inter-organizational electronic information exchange.
查看更多>>摘要：As healthcare shifts from the hospital to the home, it is becoming increasingly important to understand how patients interact with home medical devices, to inform the safe and patient-friendly design of these devices. Distributed Cognition (DCog) has been a useful theoretical framework for understanding situated interactions in the healthcare domain. However, it has not previously been applied to study interactions with home medical devices. In this study, DCog was applied to understand renal patients' interactions with Home Hemodialysis Technology (HHT), as an example of a home medical device. Data was gathered through ethnographic observations and interviews with 19 renal patients and interviews with seven professionals. Data was analyzed through the principles summarized in the Distributed Cognition for Teamwork methodology. In this paper we focus on the analysis of system activities, information flows, social structures, physical layouts, and artefacts. By explicitly considering different ways in which cognitive processes are distributed, the DCog approach helped to understand patients' interaction strategies, and pointed to design opportunities that could improve patients' experiences of using HHT. The findings highlight the need to design HHT taking into consideration likely scenarios of use in the home and of the broader home context. A setting such as home hemodialysis has the characteristics of a complex and safety-critical socio-technical system, and a approach effectively helps to understand how safety is achieved or compromised in such a system. (C) 2015 The Authors. Published by Elsevier Inc.
查看更多>>摘要：Accurate estimation of energy expenditure (EE) and cardiorespiratory fitness (CRF) is a key element in determining the causal relation between aspects of human behavior related to physical activity and health. In this paper we estimate CRF without requiring laboratory protocols and personalize energy expenditure (EE) estimation models that rely on heart rate data, using CRF. CRF influences the relation between heart rate and EE. Thus, EE estimation based on heart rate typically requires individual calibration. Our modeling technique relies on a hierarchical approach using Bayesian modeling for both CRF and EE estimation models. By including CRF level in a hierarchical Bayesian model, we avoid the need for individual calibration or explicit heart rate normalization since CRF accounts for the different relation between heart rate and EE in different individuals. Our method first estimates CRF level from heart rate during low intensity activities of daily living, showing that CRF can be determined without specific protocols. Reference VO(2)max and EE were collected on a sample of 32 participants with varying CRF level. CRF estimation error could be reduced up to 27.0% compared to other models. Secondly, we show that including CRF as a group level predictor in a hierarchical model for EE estimation accounts for the relation between CRF, heart rate and EE. Thus, reducing EE estimation error by 18.2% on average. Our results provide evidence that hierarchical modeling is a promising technique for generalized CRF estimation from activities of daily living and personalized EE estimation. (C) 2015 Elsevier Inc. All rights reserved.
查看更多>>摘要：Patient recruitment is one of the most important barriers to successful completion of clinical trials and thus to obtaining evidence about new methods for prevention, diagnostics and treatment. The reason is that recruitment is effort consuming. It requires the identification of candidate patients for the trial (the population under study), and verifying for each patient whether the eligibility criteria are met. The work we describe in this paper aims to support the comparison of population under study in different trials, and the design of eligibility criteria for new trials. We do this by introducing structured eligibility criteria, that enhance reuse of criteria across trials. We developed a method that allows for automated structuring of criteria from text. Additionally, structured eligibility criteria allow us to propose suggestions for relaxation of criteria to remove potentially unnecessarily restrictive conditions. We thereby increase the recruitment potential and generalizability of a trial.
Kim, DokyoonLi, RuowangDudek, Scott M.Ritchie, Marylyn D....
9页
查看更多>>摘要：Evaluation of survival models to predict cancer patient prognosis is one of the most important areas of emphasis in cancer research. A binary classification approach has difficulty directly predicting survival due to the characteristics of censored observations and the fact that the predictive power depends on the threshold used to set two classes. In contrast, the traditional Cox regression approach has some drawbacks in the sense that it does not allow for the identification of interactions between genomic features, which could have key roles associated with cancer prognosis. In addition, data integration is regarded as one of the important issues in improving the predictive power of survival models since cancer could be caused by multiple alterations through meta-dimensional genomic data including genome, epigenome, transcriptome, and proteome. Here we have proposed a new integrative framework designed to perform these three functions simultaneously: (1) predicting censored survival data; (2) integrating meta-dimensional omics data; (3) identifying interactions within/between meta-dimensional genomic features associated with survival. In order to predict censored survival time, martingale residuals were calculated as a new continuous outcome and a new fitness function used by the grammatical evolution neural network (GENN) based on mean absolute difference of martingale residuals was implemented. To test the utility of the proposed framework, a simulation study was conducted, followed by an analysis of meta-dimensional omics data including copy number, gene expression, DNA methylation, and protein expression data in breast cancer retrieved from The Cancer Genome Atlas (TCGA). On the basis of the results from breast cancer dataset, we were able to identify interactions not only within a single dimension of genomic data but also between meta-dimensional omics data that are associated with survival. Notably, the predictive power of our best meta-dimensional model was 73% which outperformed all of the other models conducted based on a single dimension of genomic data. Breast cancer is an extremely heterogeneous disease and the high levels of genomic diversity within/between breast tumors could affect the risk of therapeutic responses and disease progression. Thus, identifying interactions within/between meta-dimensional omics data associated with survival in breast cancer is expected to deliver direction for improved meta-dimensional prognostic biomarkers and therapeutic targets. (C) 2015 The Authors. Published by Elsevier Inc.
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