查看更多>>摘要：Risk sharing arrangements between hospitals and payers together with penalties imposed by the Centers for Medicare and Medicaid (CMS) are driving an interest in decreasing early readmissions. There are a number of published risk models predicting 30 day readmissions for particular patient populations, however they often exhibit poor predictive performance and would be unsuitable for use in a clinical setting. In this work we describe and compare several predictive models, some of which have never been applied to this task and which outperform the regression methods that are typically applied in the healthcare literature. In addition, we apply methods from deep learning to the five conditions CMS is using to penalize hospitals, and offer a simple framework for determining which conditions are most cost effective to target. (C) 2015 The Authors. Published by Elsevier Inc.
Costa, Gabriella Castro B.Braga, ReginaDavid, Jose Maria N.Campos, Fernanda...
26页
查看更多>>摘要：Context: Most specialized users (scientists) that use bioinformatics applications do not have suitable training on software development. Software Product Line (SPL) employs the concept of reuse considering that it is defined as a set of systems that are developed from a common set of base artifacts. In some contexts, such as in bioinformatics applications, it is advantageous to develop a collection of related software products, using SPL approach. If software products are similar enough, there is the possibility of predicting their commonalities, differences and then reuse these common features to support the development of new applications in the bioinformatics area.
Saleem, KashifSilva, Bruno M. C.Rodrigues, Joel J. P. C.de la Torre Diez, Isabel...
8页
查看更多>>摘要：Health telematics is a growing up issue that is becoming a major improvement on patient lives, especially in elderly, disabled, and chronically ill. In recent years, information and communication technologies improvements, along with mobile Internet, offering anywhere and anytime connectivity, play a key role on modern healthcare solutions. In this context, mobile health (m-Health) delivers healthcare services, overcoming geographical, temporal, and even organizational barriers. M-Health solutions address emerging problems on health services, including, the increasing number of chronic diseases related to lifestyle, high costs of existing national health services, the need to empower patients and families to self-care and handle their own healthcare, and the need to provide direct access to health services, regardless of time and place. Then, this paper presents a comprehensive review of the state of the art on m-Health services and applications. It surveys the most significant research work and presents a deep analysis of the top and novel m-Health services and applications proposed by industry. A discussion considering the European Union and United States approaches addressing the m-Health paradigm and directives already published is also considered. Open and challenging issues on emerging m-Health solutions are proposed for further works. (C). 2015 Elsevier Inc. All rights reserved.
查看更多>>摘要：Microarray platforms enable the investigation of allelic variants that may be correlated to phenotypes. Among those, the Affymetrix DMET (Drug Metabolism Enzymes and Transporters) platform enables the simultaneous investigation of all the genes that are related to drug absorption, distribution, metabolism and excretion (ADME). Although recent studies demonstrated the effectiveness of the use of DMET data for studying drug response or toxicity in clinical studies, there is a lack of tools for the automatic analysis of DMET data. In a previous work we developed DMET-Analyzer, a methodology and a supporting platform able to automatize the statistical study of allelic variants, that has been validated in several clinical studies. Although DMET-Analyzer is able to correlate a single variant for each probe (related to a portion of a gene) through the use of the Fisher test, it is unable to discover multiple associations among allelic variants, due to its underlying statistic analysis strategy that focuses on a single variant for each time. To overcome those limitations, here we propose a new analysis methodology for DMET data based on Association Rules mining, and an efficient implementation of this methodology, named DMET-Miner. DMET-Miner extends the DMET-Analyzer tool with data mining capabilities and correlates the presence of a set of allelic variants with the conditions of patient's samples by exploiting association rules. To face the high number of frequent itemsets generated when considering large clinical studies based on DMET data, DMET-Miner uses an efficient data structure and implements an optimized search strategy that reduces the search space and the execution time. Preliminary experiments on synthetic DMET datasets, show how DMET-Miner outperforms off-the-shelf data mining suites such as the FP-Growth algorithms available in Weka and RapidMiner. To demonstrate the biological relevance of the extracted association rules and the effectiveness of the proposed approach from a medical point of view, some preliminary studies on a real clinical dataset are currently under medical investigation. (C) 2015 Elsevier Inc. All rights reserved.
查看更多>>摘要：While health information technologies have become increasingly popular, many have not been formally tested to ascertain their usability. Traditional rigorous methods take significant amounts of time and manpower to evaluate the usability of a system. In this paper, we evaluate the use of instant data analysis (IDA) as developed by Kjeldskov et al. to perform usability testing on a tool designed for older adults and caregivers. The IDA method is attractive because it takes significantly less time and manpower than the traditional usability testing methods. In this paper we demonstrate how IDA was used to evaluate usability of a multifunctional wellness tool, discuss study results and lessons learned while using this method. We also present findings from an extension of the method which allows the grouping of similar usability problems in an efficient manner. We found that the IDA method is a quick, relatively easy approach to identifying and ranking usability issues among health information technologies. (C) 2015 Elsevier Inc. All rights reserved.
Denny, Joshua C.Spickard, AndersonSpeltz, Peter J., IIIPorier, Renee...
8页
查看更多>>摘要：Objective: Assessment of medical trainee learning through pre-defined competencies is now commonplace in schools of medicine. We describe a novel electronic advisor system using natural language processing (NLP) to identify two geriatric medicine competencies from medical student clinical notes in the electronic medical record: advance directives (AD) and altered mental status (AMS).
查看更多>>摘要：Identifying the genes that cause disease is one of the most challenging issues to establish the diagnosis and treatment quickly. Several interesting methods have been introduced for disease gene identification for a decade. In general, the main differences between these methods are the type of data used as a prior-knowledge, as well as machine learning (ML) methods used for identification. The disease gene identification task has been commonly viewed by ML methods as a binary classification problem (whether any gene is disease or not). However, the nature of the data (since there is no negative data available for training or leaners) creates a major problem which affect the results. In this paper, sequence-based, one class classification method is introduced to assign genes to disease class (yes, no). First, to generate feature vector, the sequences of proteins (genes) are initially transformed to numerical vector using physicochemical properties of amino acid. Second, as there is no definite approach to define non-disease genes (negative data); we have attempted to model solely disease genes (positive data) to make a prediction by employing Support Vector Data Description algorithm. The experimental results confirm the efficiency of the method with precision, recall and F-measure of 79.3%, 82.6% and 80.9%, respectively. (C) 2015 Elsevier Inc. All rights reserved.
查看更多>>摘要：This research aims to depict the methodological steps and tools about the combined operation of case-based reasoning (CBR) and multi-agent system (MAS) to expose the ontological application in the field of clinical decision support. The multi-agent architecture works for the consideration of the whole cycle of clinical decision-making adaptable to many medical aspects such as the diagnosis, prognosis, treatment, therapeutic monitoring of gastric cancer. In the multi-agent architecture, the ontological agent type employs the domain knowledge to ease the extraction of similar clinical cases and provide treatment suggestions to patients and physicians. Ontological agent is used for the extension of domain hierarchy and the interpretation of input requests. Case-based reasoning memorizes and restores experience data for solving similar problems, with the help of matching approach and defined interfaces of ontologies. A typical case is developed to illustrate the implementation of the knowledge acquisition and restitution of medical experts. (C) 2015 Elsevier Inc. All rights reserved.
查看更多>>摘要：The advances achieved in Natural Language Processing make it possible to automatically mine information from electronically created documents. Many Natural Language Processing methods that extract information from texts make use of annotated corpora, but these are scarce in the clinical domain due to legal and ethical issues. In this paper we present the creation of the IxaMed-GS gold standard composed of real electronic health records written in Spanish and manually annotated by experts in pharmacology and pharmacovigilance. The experts mainly annotated entities related to diseases and drugs, but also relationships between entities indicating adverse drug reaction events. To help the experts in the annotation task, we adapted a general corpus linguistic analyzer to the medical domain. The quality of the annotation process in the IxaMed-GS corpus has been assessed by measuring the inter-annotator agreement, which was 90.53% for entities and 82.86% for events. In addition, the corpus has been used for the automatic extraction of adverse drug reaction events using machine learning. (C) 2015 Elsevier Inc. All rights reserved.
Mugzach, OmriPeleg, MorBagley, Steven C.Guter, Stephen J....
15页
查看更多>>摘要：Objective: Our goal is to create an ontology that will allow data integration and reasoning with subject data to classify subjects, and based on this classification, to infer new knowledge on Autism Spectrum Disorder (ASD) and related neurodevelopmental disorders (NDD). We take a first step toward this goal by extending an existing autism ontology to allow automatic inference of ASD phenotypes and Diagnostic & Statistical Manual of Mental Disorders (DSM) criteria based on subjects' Autism Diagnostic Interview-Revised (ADI-R) assessment data.
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Elsevier