Journal of Biomedical Informatics

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• Much needed information for researchers to manage Protected Health Information.

Protecting personally identifiable information is important in clinical research. The authors, two faculty members involved in developing and implementing research infrastructure for a medical school, observed challenges novice ...

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• A framework for selecting univariate and temporal algorithms is proposed.
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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 ...

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• Data elements representing patient care teams of complex patients are presented.

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 ...

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• Detect Adverse Drug Events from the pure prescription data without additional information.

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 ...

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• Order logs of laboratory tests can indicate when to re-test laboratory tests.
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Most laboratory results are valid for only a certain time period (laboratory tests shelf-life), after which they are outdated and the test needs to be re-administered.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 ...

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• Patient clusters based on their genomic makeup.
• Discovery of significant ...

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 ...

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• We highlight the issue of lacking variance in biomedical data.
• We inform the ...

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 ...

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• Comparative analysis of deep neural network (DNN) transferability across hospitals.

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 ...

Figure the d-index comparisons of deep neural network (DNN), extra-trees (ET) and support vector machine (SVM) under raw data, and RPKM, ML, DESeq, TMM normalized data of Breast, Kidney and Prostate data. The best diagnosis ...

• The d-index comparisons of raw data and normalized data under DNN, ET and SVM.
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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. ...

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• Intensive longitudinal data leads to more precise behavioral interventions.
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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 ...

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• N-trie, a new hash trie IE rule engine designed for clinical NLP, is introduced.

To develop and evaluate an efficient Trie structure for large-scale, rule-based clinical natural language processing (NLP), which we call n-trie.

Despite the popularity of ...

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• Developed an open source diabetes-specific web portal along with drug discovery tools.

Molecular Property Diagnostic Suite - Diabetes Mellitus (MPDS^DM) 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 ...

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• Cognitive systems engineering was applied to medical referral management.
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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 ...

Fig. 1. The workflow of the synergy scoring system of drug combination.

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• Drug combination can significant boot the efficacy of the therapy.
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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 ...

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• The knowledge-driven workflow model and actual practice significantly differed.

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 ...

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• Relief-based feature selection (RBAs) efficiently detect feature interactions.
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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 ...

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• Current machine translation systems lack sufficient accuracy for clinical deployment.

To (1) characterize how machine translation (MT) is being developed to overcome language barriers in health settings; and (2) based on evaluations presented in the literature, determine which MT approaches show ...

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• Relief-based feature selection methods (RBAs) are reviewed in detailed context.

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 ...