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Journal of Biomedical Informatics

Balazs Harangi
Skin cancer is a major public health problem with over 123,000 newly diagnosed cases worldwide in every year. Melanoma is the deadliest form of skin cancer, responsible for over 9,000 deaths in the United States each year. Thus, reliable automatic melanoma screening systems would provide a great help for clinicians to detect the malignant skin lesions as early as possible. In the last five years, the efficiency of deep learning-based methods increased dramatically and their performances seem to outperform conventional image processing methods in classification tasks...
August 10, 2018: Journal of Biomedical Informatics
Yu Tian, Yong Shang, Dan-Yang Tong, Sheng-Qiang Chi, Jun Li, Xiang-Xing Kong, Ke-Feng Ding, Jing-Song Li
BACKGROUND AND OBJECTIVE: Clinical prognosis prediction plays an important role in clinical research and practice. The construction of prediction models based on electronic health record data has recently become a research focus. Due to the lack of external validation, prediction models based on single-center, hospital-specific datasets may not perform well with datasets from other medical institutions. Therefore, research investigating prognosis prediction model construction based on a collaborative analysis of multi-center electronic health record data could increase the number and coverage of patients used for model training, enrich patient prognostic features and ultimately improve the accuracy and generalization of prognosis prediction...
August 10, 2018: Journal of Biomedical Informatics
Anamika Singh Gaur, Selvaraman Nagamani, Karunakar Tanneeru, Dmitry Druzhilovskiy, Anastassia Rudik, Vladimir Poroikov, G Narahari Sastry
Molecular Property Diagnostic Suite - Diabetes Mellitus (MPDSDM ) is a Galaxy-based, open source disease-specific web portal for diabetes. It consists of three modules namely i) data library ii) data processing and iii) data analysis tools. The data library (target library and literature) module provide extensive and curated information about the genes involved in type 1 and type 2 diabetes onset and progression stage (available at The database also contains information on drug targets, biomarkers, therapeutics and associated genes specific to type 1, and type 2 diabetes...
August 6, 2018: Journal of Biomedical Informatics
Céline Faverjon, John Berezowski
There is an extensive list of methods available for the early detection of an epidemic signal in syndromic surveillance data. However, there is no commonly accepted classification system for the statistical methods used for event detection in syndromic surveillance. Comparing and choosing appropriate event detection algorithms is an increasingly challenging task. Although lists of selection criteria, and statistical methods used for signal detection have been reported, selection criteria are rarely linked to a specific set of appropriate statistical methods...
August 6, 2018: Journal of Biomedical Informatics
Jianlin Shi, John F Hurdle
OBJECTIVE: To develop and evaluate an efficient Trie structure for large-scale, rule-based clinical natural language processing (NLP), which we call n-trie. BACKGROUND: Despite the popularity of machine learning techniques in natural language processing, rule-based systems boast important advantages: distinctive transparency, ease of incorporating external knowledge, and less demanding annotation requirements. However, processing efficiency remains a major obstacle for adopting standard rule-base NLP solutions in big data analyses...
August 6, 2018: Journal of Biomedical Informatics
Guocai Chen, Alex Tsoi, Hua Xu, W Jim Zheng
The synergistic effect of drug combination is one of the most desirable properties for treating cancer. However, systematically predicting effective drug combination is a significant challenge. We report here a novel method based on deep belief network to predict drug synergy from gene expression, pathway and the Ontology Fingerprints-a literature derived ontological profile of genes. Using data sets provided by 2015 DREAM competition, our analysis shows that this integrative method outperforms published results from the DREAM website for 4999 drug pairs, demonstrating the feasibility of predicting drug synergy from literature and the -omics data using advanced artificial intelligence approach...
August 3, 2018: Journal of Biomedical Informatics
Vincent Berardi, Ricardo Carretero-González, John Bellettiere, Marc A Adams, Suzanne Hughes, Melbourne Hovell
Health interventions using real-time sensing technology are characterized by intensive longitudinal data, which has the potential to enable nuanced evaluations of individuals' responses to treatment. Existing analytic tools were not developed to capitalize on this opportunity as they typically focus on first-order findings such as changes in the level and/or slope of outcome variables over different intervention phases. This paper introduces an exploratory, Markov-based empirical transition method that offers a more comprehensive assessment of behavioral responses when intensive longitudinal data are available...
July 31, 2018: Journal of Biomedical Informatics
Sen Yang, Aleksandra Sarcevic, Richard A Farneth, Shuhong Chen, Omar Z Ahmed, Ivan Marsic, Randall S Burd
MOTIVATION: Prior research has shown that minor errors and deviations from recommended guidelines in complex medical processes can accumulate to increase the likelihood that a major error will go uncorrected and lead to an adverse outcome. Real-time automatic and accurate detection of process deviations may help medical teams better prevent or mitigate the effect of errors and improve patient outcomes. Our goal was to develop an approach for automatic detection of errors and process deviations in trauma resuscitation...
July 30, 2018: Journal of Biomedical Informatics
April Savoy, Laura G Militello, Himalaya Patel, Mindy E Flanagan, Alissa L Russ, Joanne K Daggy, Michael Weiner, Jason J Saleem
BACKGROUND: During medical referrals, communication barriers between referring and consulting outpatient clinics delay patients' access to health care. One notable opportunity for reducing these barriers is improved usefulness and usability of electronic medical consultation order forms. The cognitive systems engineering (CSE) design approach focuses on supporting humans in managing cognitive complexity in sociotechnical systems. Cognitive complexity includes communication, decision-making, problem solving, and planning...
July 30, 2018: Journal of Biomedical Informatics
M Sriram Iyengar, Harri Oinas-Kukkonen, Khin Than Win
No abstract text is available yet for this article.
July 30, 2018: Journal of Biomedical Informatics
Henry Han, Ke Men
With the surge of next generation high-throughput technologies, RNA-seq data is playing an increasingly important role in disease diagnosis, in which normalization is assumed as an essential procedure to produce comparable samples. Recent studies have seen different normalization methods proposed to remove various technical biases in RNA sequencing. However, there are no previous studies evaluating the impacts of normalization on RNA-seq disease diagnosis. In this study, we investigate this problem by analyzing structured big data: RNA-seq data acquired from the TCGA portal for its popularity in RNA-seq disease diagnosis...
July 21, 2018: Journal of Biomedical Informatics
Gal Levy-Fix, Sharon Lipsky Gorman, Jorge L Sepulveda, Noémie Elhadad
Most laboratory results are valid for only a certain time period (laboratory tests shelf-life), after which they are outdated and the test needs to be re-administered. Currently, laboratory test shelf-lives are not centrally available anywhere but the implicit knowledge of doctors. In this work we propose an automated method to learn laboratory test-specific shelf-life by identifying prevalent laboratory test order patterns in electronic health records. The resulting shelf-lives performed well in the evaluation of internal validity, clinical interpretability, and external validity...
July 20, 2018: Journal of Biomedical Informatics
Kristin N Dew, Anne M Turner, Yong K Choi, Alyssa Bosold, Katrin Kirchhoff
OBJECTIVES: To (1) characterize how machine translation (MT) is being developed to overcome language barriers in health settings; and (2) based on their evaluations, determine which MT approaches show evidence of promise and what steps need to be taken to encourage adoption of MT technologies in health settings. MATERIALS & METHODS: We performed a systematic literature search covering 2006-2016 in major health, engineering, and computer science databases. After removing duplicates, two levels of screening identified 27 articles for full text review and analysis...
July 19, 2018: Journal of Biomedical Informatics
Ryan J Urbanowicz, Melissa Meeker, William La Cava, Randal S Olson, Jason H Moore
Feature selection plays a critical role in biomedical data mining, driven by increasing feature dimensionality in target problems and growing interest in advanced but computationally expensive methodologies able to model complex associations. Specifically, there is a need for feature selection methods that are computationally efficient, yet sensitive to complex patterns of association, e.g. interactions, so that informative features are not mistakenly eliminated prior to downstream modeling. This paper focuses on Relief-based algorithms (RBAs), a unique family of filter-style feature selection algorithms that have gained appeal by striking an effective balance between these objectives while flexibly adapting to various data characteristics, e...
July 18, 2018: Journal of Biomedical Informatics
Ryan J Urbanowicz, Randal S Olson, Peter Schmitt, Melissa Meeker, Jason H Moore
Modern biomedical data mining requires feature selection methods that can (1) be applied to large scale feature spaces (e.g. 'omics' data), (2) function in noisy problems, (3) detect complex patterns of association (e.g. gene-gene interactions), (4) be flexibly adapted to various problem domains and data types (e.g. genetic variants, gene expression, and clinical data) and (5) are computationally tractable. To that end, this work examines a set of filter-style feature selection algorithms inspired by the 'Relief' algorithm, i...
July 17, 2018: Journal of Biomedical Informatics
Hamed Hassanzadeh, Anthony Nguyen, Sarvnaz Karimi, Kevin Chu
OBJECTIVE: Application of machine learning techniques for automatic and reliable classification of clinical documents have shown promising results. However, machine learning models require abundant training data specific to each target hospital and may not be able to benefit from available labeled data from each of the hospitals due to data variations. Such training data limitations have presented one of the major obstacles for maximising potential application of machine learning approaches in the healthcare domain...
July 16, 2018: Journal of Biomedical Informatics
Christian Lopez, Scott Tucker, Tarik Salameh, Conrad Tucker
INTRODUCTION: Many chronic disorders have genomic etiology, disease progression, clinical presentation, and response to treatment that vary on a patient-to-patient basis. Such variability creates a need to identify characteristics within patient populations that have clinically relevant predictive value in order to advance personalized medicine. Unsupervised machine learning methods are suitable to address this type of problem, in which no a priori class label information is available to guide this search...
July 14, 2018: Journal of Biomedical Informatics
Chen Zhan, Elizabeth Roughead, Lin Liu, Nicole Pratt, Jiuyong Li
Drug safety issues such as Adverse Drug Events (ADEs) can cause serious consequences for the public. The clinical trials that are undertaken to assess medicine efficacy and safety prior to marketing, generally, may provide sufficient samples for discovering common ADEs. However, more samples are needed to detect infrequent and rare events. Additionally, clinical trials may not include all subgroups of patients. For these reasons, post-marketing surveillance of medicines is necessary for identifying drug safety issues...
July 14, 2018: Journal of Biomedical Informatics
Konstantinos Pliakos, Celine Vens
The volume of biomedical data available to the machine learning community grows very rapidly. A rational question is how informative these data really are or how discriminant the features describing the data instances are. Several biomedical datasets suffer from lack of variance in the instance representation, or even worse, contain instances with identical features and different class labels. Indisputably, this directly affects the performance of machine learning algorithms, as well as the ability to interpret their results...
July 13, 2018: Journal of Biomedical Informatics
Pallavi Ranade-Kharkar, Scott P Narus, Gary L Anderson, Teresa Conway, Guilherme Del Fiol
OBJECTIVE: Seamless access to information about the individuals and organizations involved in the care of a specific patient ("care teams") is crucial to effective and efficient care coordination. This is especially true for vulnerable and complex patient populations such as pediatric patients with special needs. Despite wide adoption of electronic health records (EHR), current EHR systems do not adequately support the visualization and management of care teams within and across health care organizations...
July 12, 2018: Journal of Biomedical Informatics
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