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Journal of Biomedical Informatics

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https://www.readbyqxmd.com/read/29233669/using-voice-to-create-hospital-progress-notes-description-of-a-mobile-application-and-supporting-system-integrated-with-a-commercial-electronic-health-record
#1
Thomas H Payne, W David Alonso, J Andrew Markiel, Kevin Lybarger, Andrew A White
We describe the development and design of a smartphone app-based system to create inpatient progress notes using voice, commercial automatic speech recognition software, with text processing to recognize spoken voice commands and format the note, and integration with a commercial EHR. This new system fits hospital rounding workflow and was used to support a randomized clinical trial testing whether use of voice to create notes improves timeliness of note availability, note quality, and physician satisfaction with the note creation process...
December 9, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29224856/controlled-searching-in-reversibly-de-identified-medical-imaging-archives
#2
Jorge Miguel Silva, Eduardo Pinho, Eriksson Monteiro, João Figueira Silva, Carlos Costa
Nowadays, digital medical imaging in healthcare has become a fundamental tool for medical diagnosis. This growth has been accompanied by the development of technologies and standards, such as the DICOM standard and PACS. This environment led to the creation of collaborative projects where there is a need to share medical data between different institutions for research and educational purposes. In this context, it is necessary to maintain patient data privacy and provide an easy and secure mechanism for authorized personnel access...
December 7, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29224855/finding-representative-electrocardiogram-beat-morphologies-with-cur
#3
Emily P Hendryx, Béatrice M Rivière, Danny C Sorensen, Craig G Rusin
In this paper, we use the CUR matrix factorization as a means of dimension reduction to identify important subsequences in electrocardiogram (ECG) time series. As opposed to other factorizations typically used in dimension reduction that characterize data in terms of abstract representatives (for example, an orthogonal basis), the CUR factorization describes the data in terms of actual instances within the original data set. Therefore, the CUR characterization can be directly related back to the clinical setting...
December 7, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29223464/data-journeys-identifying-social-and-technical-barriers-to-data-movement-in-large-complex-organisations
#4
Iliada Eleftheriou, Suzanne M Embury, Rebecca Moden, Peter Dobinson, Andrew Brass
Managers in complex organisations often have to make decisions on whether new software developments are worth undertaking or not. Such decisions are hard to make, especially at an enterprise level. Both costs and risks are regularly underestimated, despite the existence of a plethora of software and systems engineering methodologies aimed at predicting and controlling them. Our objective is to help managers and stakeholders of large, complex organisations (like the National Health Service in the UK) make better informed decisions on the costs and risks of planned new software systems that will reuse or extend their existing information infrastructure...
December 6, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29197649/heritable-genotype-contrast-mining-reveals-novel-gene-associations-specific-to-autism-subgroups
#5
Matt Spencer, Nicole Takahashi, Sounak Chakraborty, Judith Miles, Chi-Ren Shyu
Though the genetic etiology of autism is complex, our understanding can be improved by identifying genes and gene-gene interactions that contribute to the development of specific autism subtypes. Identifying such gene groupings will allow individuals to be diagnosed and treated according to their precise characteristics. To this end, we developed a method to associate gene combinations with groups with shared autism traits, targeting genetic elements that distinguish patient populations with opposing phenotypes...
November 29, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29196114/flexible-cluster-based-analysis-of-the-electronic-medical-record-of-sepsis-with-composite-mixture-models
#6
Michael B Mayhew, Brenden K Petersen, Ana Paula Sales, John D Greene, Vincent X Liu, Todd S Wasson
The widespread adoption of electronic medical records (EMRs) in healthcare has provided vast new amounts of data for statistical machine learning researchers in their efforts to model and predict patient health status, potentially enabling novel advances in treatment. In the case of sepsis, a debilitating, dysregulated host response to infection, extracting subtle, uncataloged clinical phenotypes from the EMR with statistical machine learning methods has the potential to impact patient diagnosis and treatment early in the course of their hospitalization...
November 28, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29175548/radiology-report-annotation-using-intelligent-word-embeddings-applied-to-multi-institutional-chest-ct-cohort
#7
Imon Banerjee, Matthew C Chen, Matthew P Lungren, Daniel L Rubin
We proposed an unsupervised hybrid method - Intelligent Word Embedding (IWE) that combines neural embedding method with a semantic dictionary mapping technique for creating a dense vector representation of unstructured radiology reports. We applied IWE to generate embedding of chest CT radiology reports from two healthcare organizations and utilized the vector representations to semi-automate report categorization based on clinically relevant categorization related to the diagnosis of pulmonary embolism (PE)...
November 23, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29174994/learning-bundled-care-opportunities-from-electronic-medical-records
#8
You Chen, Abel N Kho, David Liebovitz, Catherine Ivory, Sarah Osmundson, Jiang Bian, Bradley A Malin
OBJECTIVE: The traditional fee-for-service approach to healthcare can lead to the management of a patient's conditions in a siloed manner, inducing various negative consequences. It has been recognized that a bundled approach to healthcare - one that manages a collection of health conditions together - may enable greater efficacy and cost savings. However, it is not always evident which sets of conditions should be managed in a bundled manner. In this study, we investigate if a data-driven approach can automatically learn potential bundles...
November 22, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29175431/a-cloud-based-framework-for-large-scale-traditional-chinese-medical-record-retrieval
#9
Lijun Liu, Li Liu, Xiaodong Fu, Qingsong Huang, Xianwen Zhang, Yin Zhang
INTRODUCTION: Electronic medical records are increasingly common in medical practice. The secondary use of medical records has become increasingly important. It relies on the ability to retrieve the complete information about desired patient populations. How to effectively and accurately retrieve relevant medical records from large- scale medical big data is becoming a big challenge. Therefore, we propose an efficient and robust framework based on cloud for large-scale Traditional Chinese Medical Records (TCMRs) retrieval...
November 21, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29162496/clinical-information-extraction-applications-a-literature-review
#10
REVIEW
Yanshan Wang, Liwei Wang, Majid Rastegar-Mojarad, Sungrim Moon, Feichen Shen, Naveed Afzal, Sijia Liu, Yuqun Zeng, Saeed Mehrabi, Sunghwan Sohn, Hongfang Liu
BACKGROUND: With the rapid adoption of electronic health records (EHRs), it is desirable to harvest information and knowledge from EHRs to support automated systems at the point of care and to enable secondary use of EHRs for clinical and translational research. One critical component used to facilitate the secondary use of EHR data is the information extraction (IE) task, which automatically extracts and encodes clinical information from text. OBJECTIVES: In this literature review, we present a review of recent published research on clinical information extraction (IE) applications...
November 18, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29158204/definition-of-a-snomed-ct-pathology-subset-and-microglossary-based-on-1-17-million-biological-samples-from-the-catalan-pathology-registry
#11
Xavier Sanz, Laura Pareja, Ariadna Rius, Pepi Rodenas, Núria Abdón, Jordi Gálvez, Laura Esteban, Josep Maria Escribà, Josep Maria Borràs, Josepa Ribes
SNOMED CT terminology is not backed by standard norms of encoding among pathologists. The vast number of concepts ordered in hierarchies and axes, together with the lack of rules of use, complicates the functionality of SNOMED CT for coding, extracting, and analyzing the data. Defining subgroups of SNOMED CT by discipline could increase its functionality. The challenge lies in how to choose the concepts to be included in a subset from a total of over 300,000. Besides, SNOMED CT does not cover daily need, as the clinical reality is dynamic and changing...
November 17, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29155333/ima-identifying-disease-related-genes-using-mesh-terms-and-association-rules
#12
Jeongwoo Kim, Changbae Bang, Hyeonseo Hwang, Doyoung Kim, Chihyun Park, Sanghyun Park
Genes play an important role in several diseases. Hence, in biology, identifying relationships between diseases and genes is important for the analysis of diseases, because mutated or dysregulated genes play an important role in pathogenesis. Here, we propose a method to identify disease-related genes using MeSH terms and association rules. We identified genes by analyzing the MeSH terms and extracted information on gene-gene interactions based on association rules. By integrating the extracted interactions, we constructed gene-gene networks and identified disease-related genes...
November 15, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29154847/behavioural-informatics-for-improving-water-hygiene-practice-based-on-iot-environment
#13
Yang Fu, Wenyan Wu
The development of Internet of Things (IoT) and latest Information and Communication Technologies (ICT) have changed the nature of healthcare monitoring and health behaviour intervention in many applications. Water hygiene and water conservation behaviour intervention as important influence factors to human health are gaining much attentions for improving sustained sanitation practice. Based on face-to-face delivery, typical behaviour intervention method is costly and hardly to provide all day access to personalised intervention guidance and feedbacks...
November 14, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29146562/multi-method-laboratory-user-evaluation-of-an-actionable-clinical-performance-information-system-implications-for-usability-and-patient-safety
#14
Benjamin Brown, Panos Balatsoukas, Richard Williams, Matthew Sperrin, Iain Buchan
INTRODUCTION: Electronic audit and feedback (e-A&F) systems are used worldwide for care quality improvement. They measure health professionals' performance against clinical guidelines, and some systems suggest improvement actions. However, little is known about optimal interface designs for e-A&F, in particular how to present suggested actions for improvement. We developed a novel theory-informed system for primary care (the Performance Improvement plaN GeneratoR; PINGR) that covers the four principal interface components: clinical performance summaries; patient lists; detailed patient-level information; and suggested actions...
November 13, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29146561/recurrent-neural-networks-with-specialized-word-embeddings-for-health-domain-named-entity-recognition
#15
Iñigo Jauregi Unanue, Ehsan Zare Borzeshi, Massimo Piccardi
BACKGROUND: Previous state-of-the-art systems on Drug Name Recognition (DNR) and Clinical Concept Extraction (CCE) have focused on a combination of text "feature engineering" and conventional machine learning algorithms such as conditional random fields and support vector machines. However, developing good features is inherently heavily time-consuming. Conversely, more modern machine learning approaches such as recurrent neural networks (RNNs) have proved capable of automatically learning effective features from either random assignments or automated word "embeddings"...
November 13, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29137965/a-novel-bagging-c4-5-algorithm-based-on-wrapper-feature-selection-for-supporting-wise-clinical-decision-making
#16
Shin-Jye Lee, Zhaozhao Xu, Tong Li, Yun Yang
From the perspective of clinical decision-making in a Medical IoT-based healthcare system, achieving effective and efficient analysis of long-term health data for supporting wise clinical decision-making is an extremely important objective, but determining how to effectively deal with the multi-dimensionality and high volume of generated data obtained from Medical IoT-based healthcare systems is an issue of increasing importance in IoT healthcare data exploration and management. A novel classifier or predicator equipped with a good feature selection function contributes effectively to classification and prediction performance...
November 11, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29129622/personalized-long-term-prediction-of-cognitive-function-using-sequential-assessments-to-improve-model-performance
#17
Chih-Lin Chi, Wenjun Zeng, Wonsuk Oh, Soo Borson, Tatiana Lenskaia, Xinpeng Shen, Peter J Tonellato
Prediction of onset and progression of cognitive decline and dementia is important both for understanding the underlying disease processes and for planning health care for populations at risk. Predictors identified in research studies are typically accessed at one point in time. In this manuscript, we argue that an accurate model for predicting cognitive status over relatively long periods requires inclusion of time-varying components that are sequentially assessed at multiple time points (e.g., in multiple follow-up visits)...
November 9, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29127041/logical-comparison-over-rdf-resources-in-bio-informatics
#18
S Colucci, F M Donini, E Di Sciascio
Comparison of resources is a frequent task in different bio-informatics applications, including drug-target interaction, drug repositioning and mechanism of action understanding, among others. This paper proposes a general method for the logical comparison of resources modeled in Resource Description Framework and shows its distinguishing features with reference to the comparison of drugs. In particular, the method returns a description of the commonalities between resources, rather than a numerical value estimating their similarity and/or relatedness...
November 7, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29113934/validating-ehr-clinical-models-using-ontology-patterns
#19
Catalina Martínez-Costa, Stefan Schulz
Clinical models are artefacts that specify how information is structured in electronic health records (EHRs). However, the makeup of clinical models is not guided by any formal constraint beyond a semantically vague information model. We address this gap by advocating ontology design patterns as a mechanism that makes the semantics of clinical models explicit. This paper demonstrates how ontology design patterns can validate existing clinical models using SHACL. Based on the Clinical Information Modelling Initiative (CIMI), we show how ontology patterns detect both modeling and terminology binding errors in CIMI models...
November 4, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29097278/hamda-hybrid-approach-for-mirna-disease-association-prediction
#20
Xing Chen, Ya-Wei Niu, Guang-Hui Wang, Gui-Ying Yan
For decades, enormous experimental researches have collectively indicated that microRNA (miRNA) could play indispensable roles in many critical biological processes and thus also the pathogenesis of human complex diseases. Whereas the resource and time cost required in traditional biology experiments are expensive, more and more attentions have been paid to the development of effective and feasible computational methods for predicting potential associations between disease and miRNA. In this study, we developed a computational model of Hybrid Approach for MiRNA-Disease Association prediction (HAMDA), which involved the hybrid graph-based recommendation algorithm, to reveal novel miRNA-disease associations by integrating experimentally verified miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity into a recommendation algorithm...
October 30, 2017: Journal of Biomedical Informatics
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