Showing papers in "Journal of Biomedical Informatics in 2016"

TL;DR: A literature review of the usage of process mining in healthcare and the most commonly used categories and emerging topics have been identified, as well as future trends, such as enhancing Hospital Information Systems to become process-aware.

TL;DR: Use of the ISR framework is a potentially useful approach for the design of a mobile app that incorporates end-users' design preferences and has heuristic value for those venturing into the area of technology-based intervention work.

TL;DR: This work reviews and brings together the recent works carried out in the automatic stress detection looking over the measurements executed along the three main modalities, namely, psychological, physiological and behaviouralmodalities, in order to give hints about the most appropriate techniques to be used and thereby, to facilitate the development of such a holistic system.

TL;DR: Consumer-grade smart watches have penetrated the health research space rapidly since 2014 and technical function, acceptability, and effectiveness in supporting health must be validated in larger field studies that enroll actual participants living with the conditions these devices target.

TL;DR: A semi-supervised learning method for EHR phenotype extraction using denoising autoencoders for phenotype stratification and a promising approach to clarify disease subtypes and improve genotype-phenotype association studies that leverage EHRs are developed.

TL;DR: Evaluating the impact of the Learning Healthcare System instances where it is implemented could work as a catalyst in reaching higher acceptance and adoption of the proposed ideas by healthcare worldwide.

TL;DR: In this article, the authors investigated the effect of sentiment analysis features in locating adverse drug reactions (ADRs) mentions in tweets and health related forum posts and found that adding sentiment analysis feature can marginally improve the performance of even a state-of-the-art ADR identification method.

TL;DR: OSN data contain significant information that can be used to track a pandemic, but it can offer complementary data that can work best when integrated with traditional data.

TL;DR: The development and performance of an automated EWS based on EMR data, called Advanced Alert Monitor (AAM), is described, an example of a score that takes advantage of multiple data streams now available in modern EMRs and highlights the ability to harness complex algorithms to maximize signal extraction.

TL;DR: A conceptual framework and survey of the state of the art of technology-based fall prevention systems which is derived from a systematic template analysis of studies presented in contemporary research literature is presented.

TL;DR: An ensemble framework with multi-layer classification using enhanced bagging and optimized weighting is presented and it is shown that ensemble framework achieved the highest accuracy, accuracy and F-Measure when compared with individual classifiers for all the diseases.

TL;DR: An architecture as a proof-of-concept for emotion detection and regulation in smart health environments to detect the patient's emotional state by analysing his/her physiological signals, facial expression and behaviour and provides the best-tailored actions in the environment to regulate these emotions towards a positive mood when possible.

TL;DR: In this paper, a topic detection method based on paragraph vectors is proposed to accelerate citation screening in clinical and public health reviews. But the method is not suitable for the task of biomedical journal articles, since it requires expert reviewers to manually screen thousands of citations to identify all relevant articles to the review.

TL;DR: A Q-backpropagated time delay neural network (Q-BTDNN) classifier that builds a temporal classification model, which performs the task of classification and prediction in CDMS is presented, which proves the efficiency of Q-BP in terms of its improved classification accuracy.

TL;DR: The OMOP CDM best met the criteria for supporting data sharing from longitudinal EHR-based studies, and was easily adaptable to common data model evaluation for other uses.

TL;DR: A feasibility test of a multi-method approach for both data collection and data analyses for patients' experienced usability of a mHealth system for diabetes type 2 self-management yielded a more comprehensive set of usability issues.

TL;DR: This paper presents a general-purpose approach to account for right-censored outcomes using inverse probability of censoring weighting (IPCW), and illustrates how IPCW can easily be incorporated into a number of existing machine learning algorithms used to mine big health care data including Bayesian networks, k-nearest neighbors, decision trees, and generalized additive models.

TL;DR: In a meaningful CIS use situation at HEGP, this study confirms the importance of CISQ in explaining satisfaction and CI and proposes a unified metamodel of evaluation that can be adapted to each context or deployment phase of a CIS project.

TL;DR: The main conclusion of this review is that there is a great interest in the research community in the use of serious games for neuropsychological evaluation, and in accordance with the increasing number of studies published in the last three years, they demonstrate its potential as a serious alternative to classic Neuropsychological tests.

TL;DR: An online survey was developed to systematically investigate OHC personas and four personas emerged-Caretakers, Opportunists, Scientists, and Adventurers illustrating users' needs and requirements in OHC use.

TL;DR: In this paper, the authors extracted features from electronic health records (EHR) to predict diagnosed type 2 diabetes using multivariate logistic regression and a random-forests probabilistic model for out-of-sample validation.

TL;DR: Current user tracking methods for internet-based health interventions are described and suggestions for improvement are offered based on the design and pilot testing of healthMpowerment.org.

TL;DR: This study revealed that using EHR data to build prediction models can be very challenging using existing classification methods due to the high dimensionality and complexity of E HR data and proposed a probabilistic loss function to determine the large error and small error instances.

TL;DR: The main goal was to analyse how the frontal lobe of the brain works in terms of prominent cognitive skills during five types of game mechanics widely used in commercial videogames.

TL;DR: The empirical evaluation results suggest that MCLDA is capable of capturing the comorbidity structures and linking them with the distribution of medications, and outperforms alternative methods such as logistic regressions and the k-nearest-neighbor (KNN) model for two prediction tasks, i.e., medication and diagnosis prediction.

TL;DR: A machine-learning statistical model from EHR data can be useful to predict surgical complications and the combination of EHR extracted free text, blood samples values, and patient vital signs, improves the model performance.

TL;DR: This paper proposes a functional specification of gait in which only observational kinematic aspects are discussed and develops an extraction system through which image sequences are analysed to identify gait features and satisfactorily supplies a proper distinction between normal and abnormal gait.

TL;DR: The proposed hybrid Apriori algorithm could efficiently detect DIAE signals from SRS data as well as, identifying rare adverse drug reactions (ADRs) and could provide the statistical context to guard against spurious DIAEs.

TL;DR: A new gene selection method based on clustering, in which dissimilarity measures are obtained through kernel functions, which is capable of achieving better accuracies and may be an efficient tool for finding possible biomarkers from gene expression data.

TL;DR: Outlier-based alerting is supported as a promising new approach to data-driven clinical alerting that is generated automatically based on past EMR data.