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Journal of Biomedical Informatics

Laila R Bekhet, Yonghui Wu, Ningtao Wang, Xin Geng, Wenjin Jim Zheng, Fei Wang, Hulin Wu, Hua Xu, Degui Zhi
Recently, recurrent neural networks (RNNs) have been applied in predicting disease onset risks with Electronic Health Record (EHR) data. While these models demonstrated promising results on relatively small data sets, the generalizability and transferability of those models and its applicability to different patient populations across hospitals have not been evaluated. In this study, we evaluated an RNN model, RETAIN, over Cerner Health Facts® EMR data, for heart failure onset risk prediction. Our data set included over 150,000 heart failure patients and over 1,000,000 controls from nearly 400 hospitals...
June 14, 2018: Journal of Biomedical Informatics
Philip van Damme, Manuel Quesada-Martínez, Ronald Cornet, Jesualdo Tomás Fernández-Breis
Ontologies and terminologies have been identified as key resources for the achievement of semantic interoperability in biomedical domains. The development of ontologies is performed as a joint work by domain experts and knowledge engineers. The maintenance and auditing of these resources is also the responsibility of such experts, and this is usually a time-consuming, mostly manual task. Manual auditing is impractical and ineffective for most biomedical ontologies, especially for larger ones. An example is SNOMED CT, a key resource in many countries for codifying medical information...
June 13, 2018: Journal of Biomedical Informatics
Elena Tutubalina, Zulfat Miftahutdinov, Sergey Nikolenko, Valentin Malykh
Text mining of scientific libraries and social media has already proven itself as a reliable tool for drug repurposing and hypothesis generation. The task of mapping a disease mention to a concept in a controlled vocabulary, typically to the standard thesaurus in the Unified Medical Language System (UMLS), is known as medical concept normalization. This task is challenging due to the differences in the use of medical terminology between health care professionals and social media texts coming from the lay public...
June 12, 2018: Journal of Biomedical Informatics
Mozhgan Nasr Azadani, Nasser Ghadiri, Ensieh Davoodijam
OBJECTIVE: Automatic text summarization offers an efficient solution to access the ever-growing amounts of both scientific and clinical literature in the biomedical domain by summarizing the source documents while maintaining their most informative contents. In this paper, we propose a novel graph-based summarization method that takes advantage of the domain-specific knowledge and a well-established data mining technique called frequent itemset mining. METHODS: Our summarizer exploits the Unified Medical Language System (UMLS) to construct a concept-based model of the source document and mapping the document to the concepts...
June 12, 2018: Journal of Biomedical Informatics
Jingfeng Chen, Leilei Sun, Chonghui Guo, Wei Wei, Yanming Xie
BACKGROUND: A clinical pathway (CP) defines a standardized care process for a well-defined patient group that aims to improve patient outcomes and promote patient safety. However, the construction of a new pathway from scratch is a time-consuming task for medical staff because it involves many factors, including objects, multidisciplinary collaboration, sequential design, and outcome measurements. Recently, the rapid development of hospital information systems has allowed the storage of large volumes of electronic medical records (EMRs), and this information constitutes an abundant data resource for building CPs using data-mining methods...
June 11, 2018: Journal of Biomedical Informatics
Mohamed Abdelhamid
Health information exchanges (HIEs) are multisided platforms that facilitate the sharing of patient health information (PHI) between providers and payers across organizations within a region, community or hospital system. The benefits of HIEs to payers and providers include lower cost, faster services, and better health outcome. However, most HIEs have configured the patient healthcare consent process to give all providers who sign up with the exchange access to PHI for all consenting patients, leaving no control to patients in customized what information to share and with who...
June 9, 2018: Journal of Biomedical Informatics
Smaranda Belciug, Florin Gorunescu
Methods based on microarrays (MA), mass spectrometry (MS), and machine learning (ML) algorithms have evolved rapidly in recent years, allowing for early detection of several types of cancer. A pitfall of these approaches, however, is the overfitting of data due to large number of attributes and small number of instances -- a phenomenon known as the 'curse of dimensionality'. A potentially fruitful idea to avoid this drawback is to develop algorithms that combine fast computation with a filtering module for the attributes...
June 8, 2018: Journal of Biomedical Informatics
Stephen Wu, Sijia Liu, Sunghwan Sohn, Sungrim Moon, Chung-Il Wi, Young Juhn, Hongfang Liu
Sequences of events have often been modeled with computational techniques, but typical preprocessing steps and problem settings do not explicitly address the ramifications of timestamped events. Clinical data, such as is found in electronic health records (EHRs), typically comes with timestamp information. In this work, we define event sequences and their properties: synchronicity, evenness, and co-cardinality; we then show how asynchronous, uneven, and multi-cardinal problem settings can support explicit accountings of relative time...
June 5, 2018: Journal of Biomedical Informatics
Sanjay Purushotham, Chuizheng Meng, Zhengping Che, Yan Liu
Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications. However, few works exist which have benchmarked the performance of the deep learning models with respect to the state-of-the-art machine learning models and prognostic scoring systems on publicly available healthcare datasets. In this paper, we present the benchmarking results for several clinical prediction tasks such as mortality prediction, length of stay prediction, and ICD-9 code group prediction using Deep Learning models, ensemble of machine learning models (Super Learner algorithm), SAPS II and SOFA scores...
June 4, 2018: Journal of Biomedical Informatics
E Parimbelli, S Marini, L Sacchi, R Bellazzi
Evidence-based medicine is the most prevalent paradigm adopted by physicians. Clinical practice guidelines typically define a set of recommendations together with eligibility criteria that restrict their applicability to a specific group of patients. The ever-growing size and availability of health-related data is currently challenging the broad definitions of guideline-defined patient groups. Precision medicine leverages on genetic, phenotypic, or psychosocial characteristics to provide precise identification of patient subsets for treatment targeting...
June 1, 2018: Journal of Biomedical Informatics
Thomas Ly, Carol Pamer, Oanh Dang, Sonja Brajovic, Shahrukh Haider, Taxiarchis Botsis, David Milward, Andrew Winter, Susan Lu, Robert Ball
INTRODUCTION: The FDA Adverse Event Reporting System (FAERS) is a primary data source for identifying unlabeled adverse events (AEs) in a drug or biologic drug product's postmarketing phase. Many AE reports must be reviewed by drug safety experts to identify unlabeled AEs, even if the reported AEs are previously identified, labeled AEs. Integrating the labeling status of drug product AEs into FAERS could increase report triage and review efficiency. Medical Dictionary for Regulatory Activities (MedDRA) is the standard for coding AE terms in FAERS cases...
May 31, 2018: Journal of Biomedical Informatics
Houcemeddine Turki, Mohamed Ali Hadj Taieb, Mohamed Ben Aouicha
No abstract text is available yet for this article.
May 29, 2018: Journal of Biomedical Informatics
Tiago K Colicchio, Guilherme Del Fiol, Debra L Scammon, Julio C Facelli, Watson A Bowes, Scott P Narus
OBJECTIVE: To test a systematic methodology to monitor longitudinal change patterns on quality, productivity, and safety outcomes during a large-scale commercial Electronic Health Record (EHR) implementation. MATERIALS AND METHODS: Our method combines an interrupted time-series design with control sites and 41 consensus outcomes including quality (11 measures), productivity (20 measures), and safety (10 measures). The intervention consisted of a phased commercial EHR implementation at a large health care delivery network...
May 29, 2018: Journal of Biomedical Informatics
Alessia Paglialonga, Alessandra Lugo, Eugenio Santoro
The need to characterize and assess health apps has inspired a significant amount of research in the past years, in search for methods able to provide potential app users with relevant, meaningful knowledge. This article presents an overview of the recent literature in this field and categorizes - by discussing some specific examples - the various methodologies introduced so far for the identification, characterization, and assessment of health apps. Specifically, this article outlines the most significant web-based resources for app identification, relevant frameworks for descriptive characterization of apps' features, and a number of methods for the assessment of quality along its various components (e...
May 28, 2018: Journal of Biomedical Informatics
Lang He, Cui Cao
To help clinicians to efficiently diagnose the severity of a person's depression, the affective computing community and the artificial intelligence field have shown a growing interest in designing automated systems. The speech features have useful information for the diagnosis of depression. However, manually designing and domain knowledge are still important for the selection of the feature, which makes the process labor consuming and subjective. In recent years, deep-learned features based on neural networks have shown superior performance to hand-crafted features in various areas...
May 28, 2018: Journal of Biomedical Informatics
Ling Zheng, Yan Chen, Gai Elhanan, Yehoshua Perl, James Geller, Christopher Ochs
In previous research, we have demonstrated for a number of ontologies that structurally complex concepts (for different definitions of "complex") in an ontology are more likely to exhibit errors than other concepts. Thus, such complex concepts often become fertile ground for quality assurance (QA) in ontologies. They should be audited first. One example of complex concepts is given by "overlapping concepts" (to be defined below.) Historically, a different auditing methodology had to be developed for every single ontology...
May 28, 2018: Journal of Biomedical Informatics
Giorgio Leonardi, Manuel Striani, Silvana Quaglini, Anna Cavallini, Stefania Montani
Many medical information systems record data about the executed process instances in the form of an event log. In this paper, we present a framework, able to convert actions in the event log into higher level concepts, at different levels of abstraction, on the basis of domain knowledge. Abstracted traces are then provided as an input to trace comparison and semantic process discovery. Our abstraction mechanism is able to manage non trivial situations, such as interleaved actions or delays between two actions that abstract to the same concept...
May 21, 2018: Journal of Biomedical Informatics
M S Cheng Ye, Daniel Fabbri
OBJECTIVE: Word embeddings project semantically similar terms into nearby points in a vector space. When trained on clinical text, these embeddings can be leveraged to improve keyword search and text highlighting. In this paper, we present methods to refine the selection process of similar terms from multiple EMR-based word embeddings, and evaluate their performance quantitatively and qualitatively across multiple chart review tasks. MATERIALS AND METHODS: Word embeddings were trained on each clinical note type in an EMR...
May 21, 2018: Journal of Biomedical Informatics
Feng Wang, Jing-Yi Zhou, Yu Tian, Yu Wang, Ping Zhang, Jiang-Hua Chen, Jing-Song Li
End-stage renal disease (ESRD) is the final stage of chronic kidney disease (CKD) and requires hemodialysis (HD) for survival. Intradialytic blood pressure (IBP) measurements are necessary to ensure patient safety during HD treatments and have critical clinical and prognostic significance. Studies on IBP measurements, especially IBP patterns, are limited. All related studies have been based on a priori knowledge and artificially classified IBP patterns. Therefore, the results were influenced by subjective concepts...
May 21, 2018: Journal of Biomedical Informatics
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