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Journal of biomedical informatics 22건

  1. [해외논문]   Cover 2: Editorial Board   SCI SCIE


    Journal of biomedical informatics v.79 ,pp. IFC - IFC , 2018 , 1532-0464 ,

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    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  2. [해외논문]   Cover 2: Editorial Board  


    Journal of biomedical informatics v.79 ,pp. IFC , 2018 , 1532-0464 ,

    초록

    원문보기

    원문보기
    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  3. [해외논문]   Cover 1/Spine   SCI SCIE


    Journal of biomedical informatics v.79 ,pp. OFC - OFC , 2018 , 1532-0464 ,

    초록

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    원문보기
    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  4. [해외논문]   fmi-ii: Table of Contents   SCI SCIE


    Journal of biomedical informatics v.79 ,pp. i - ii , 2018 , 1532-0464 ,

    초록

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    원문보기
    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  5. [해외논문]   Are privacy-enhancing technologies for genomic data ready for the clinic? A survey of medical experts of the Swiss HIV Cohort Study   SCI SCIE

    Raisaro, Jean-Louis (School of Computer Communications Sciences, École Polytechnique Fédérale de Lausanne, Switzerland ) , McLaren, Paul J. (J.C. Wilt Infectious Diseases Research Centre, National Microbiology Laboratories, Public Health Agency of Canada, Winnipeg, Canada ) , Fellay, Jacques (School of Life Sciences, École Polytechnique Fédérale de Lausanne, Switzerland ) , Cavassini, Matthias (Division of Infectious Diseases, Lausanne University Hospital, Switzerland ) , Klersy, Catherine (Service of Biometry and Clinical Epidemiology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy ) , Hubaux, Jean-Pierre (School of Computer Communications Sciences, École Polytechnique Fédérale de Lausanne, Switzerland)
    Journal of biomedical informatics v.79 ,pp. 1 - 6 , 2018 , 1532-0464 ,

    초록

    Abstract Purpose Protecting patient privacy is a major obstacle for the implementation of genomic-based medicine. Emerging privacy-enhancing technologies can become key enablers for managing sensitive genetic data. We studied physicians’ attitude toward this kind of technology in order to derive insights that might foster their future adoption for clinical care. Methods We conducted a questionnaire-based survey among 55 physicians of the Swiss HIV Cohort Study who tested the first implementation of a privacy-preserving model for delivering genomic test results. We evaluated their feedback on three different aspects of our model: clinical utility, ability to address privacy concerns and system usability. Results 38/55 (69%) physicians participated in the study. Two thirds of them acknowledged genetic privacy as a key aspect that needs to be protected to help building patient trust and deploy new-generation medical information systems. All of them successfully used the tool for evaluating their patients’ pharmacogenomics risk and 90% were happy with the user experience and the efficiency of the tool. Only 8% of physicians were unsatisfied with the level of information and wanted to have access to the patient’s actual DNA sequence. Conclusion This survey, although limited in size, represents the first evaluation of privacy-preserving models for genomic-based medicine. It has allowed us to derive unique insights that will improve the design of these new systems in the future. In particular, we have observed that a clinical information system that uses homomorphic encryption to provide clinicians with risk information based on sensitive genetic test results can offer information that clinicians feel sufficient for their needs and appropriately respectful of patients’ privacy. The ability of this kind of systems to ensure strong security and privacy guarantees and to provide some analytics on encrypted data has been assessed as a key enabler for the management of sensitive medical information in the near future. Providing clinically relevant information to physicians while protecting patients’ privacy in order to comply with regulations is crucial for the widespread use of these new technologies. Highlights First evaluation of advanced privacy-preserving models for genomic-based testing. 38/55 physicians of the Swiss HIV Cohort Study participated in the study. Tools based on homomorphic encryption can be useful in operational settings. In addition to privacy and security, clinical utility and usability are key. Graphical abstract [DISPLAY OMISSION]

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  6. [해외논문]   User acceptance of location-tracking technologies in health research: Implications for study design and data quality   SCI SCIE

    Hardy, Jean (School of Information, University of Michigan, 3443 North Quad, 105 S. State Street, Ann Arbor, MI 48109-1285, USA ) , Veinot, Tiffany C. (School of Information, University of Michigan, 3443 North Quad, 105 S. State Street, Ann Arbor, MI 48109-1285, USA ) , Yan, Xiang (Taubman College of Architecture and Urban Planning, University of Michigan, 2223C Art and Architecture Building, 2000 Bonisteel Blvd., Ann Arbor, MI 48109-2069, USA ) , Berrocal, Veronica J. (Department of Biostatistics, School of Public Health, University of Michigan, M4525 SPH II, 1415 Washington Heights, Ann Arbor, MI 48109-2029, USA ) , Clarke, Philippa (Institute for Social Research, University of Michigan, 426 Thompson Street, 3330 ISR, Ann Arbor, MI 48106-1248, USA ) , Goodspeed, Robert (Taubman College of Architecture and Urban Planning, University of Michigan, 2223C Art and Architecture Building, 2000 Bonisteel Blvd., Ann Arbor, MI 48109-2069, USA ) , Gomez-Lopez, Iris N. (School of Information, University of Michigan, 3443 North Quad, 105 S. State Street, Ann Arbor, MI 48109-1285, USA ) , Romero, Daniel (School of Information, University of Michigan, 3) , Vydiswaran, V.G. Vinod
    Journal of biomedical informatics v.79 ,pp. 7 - 19 , 2018 , 1532-0464 ,

    초록

    Abstract Research regarding place and health has undergone a revolution due to the availability of consumer-focused location-tracking devices that reveal fine-grained details of human mobility. Such research requires that participants accept such devices enough to use them in their daily lives. There is a need for a theoretically grounded understanding of acceptance of different location-tracking technology options, and its research implications. Guided by an extended Unified Theory of Acceptance and Use of Technology (UTAUT), we conducted a 28-day field study comparing 21 chronically ill people’s acceptance of two leading, consumer-focused location-tracking technologies deployed for research purposes: (1) a location-enabled smartphone, and (2) a GPS watch/activity tracker. Participants used both, and completed two surveys and qualitative interviews. Findings revealed that all participants exerted effort to facilitate data capture, such as by incorporating devices into daily routines and developing workarounds to keep devices functioning. Nevertheless, the smartphone was perceived to be significantly easier and posed fewer usability challenges for participants than the watch. Older participants found the watch significantly more difficult to use. For both devices, effort expectancy was significantly associated with future willingness to participate in research although prosocial motivations overcame some concerns. Social influence, performance expectancy and use behavior were significantly associated with intentions to use the devices in participants’ personal lives. Data gathered via the smartphone was significantly more complete than data gathered via the watch, primarily due to usability challenges. To make longer-term participation in location tracking research a reality, and to achieve complete data capture, researchers must minimize the effort involved in participation; this requires usable devices. For long-term location-tracking studies using similar devices, findings indicate that only smartphone-based tracking is up to the challenge. Highlights 21 Chronically ill people used a smartphone and a GPS watch to track their location. Participants found the watch significantly more difficult to use than the smartphone. Due to usability issues, smartphone data was more complete than watch data. Device-based effort was associated with intention to participate in future research. Graphical abstract [DISPLAY OMISSION]

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  7. [해외논문]   A method for the analysis and visualization of clinical workflow in dynamic environments   SCI SCIE

    Vankipuram, Akshay (Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, United States ) , Traub, Stephen (Mayo Clinic, Phoenix, AZ, United States ) , Patel, Vimla L. (Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, United States)
    Journal of biomedical informatics v.79 ,pp. 20 - 31 , 2018 , 1532-0464 ,

    초록

    Abstract The analysis of clinical workflow offers many challenges, especially in settings characterized by rapid dynamic change. Typically, some combination of approaches drawn from ethnography and grounded theory-based qualitative methods are used to develop relevant metrics. Medical institutions have recently attempted to introduce technological interventions to develop quantifiable quality metrics to supplement existing purely qualitative analyses. These interventions range from automated location tracking to repositories of clinical data (e.g., electronics health record (EHR) data, medical equipment logs). Our goal in this paper is to present a cohesive framework that combines a set of analytic techniques that can potentially complement traditional human observations to derive a deeper understanding of clinical workflow and thereby to enhance the quality, safety, and efficiency of care offered in that environment. We present a series of theoretically-guided techniques to perform analysis and visualization of data developed using location tracking, with illustrations using the Emergency Department (ED) as an example. Our framework is divided into three modules: (i) transformation, (ii) analysis, and (iii) visualization. We describe the methods used in each of these modules, and provide a series of visualizations developed using location-tracking data collected at the Mayo Clinic ED (Phoenix, AZ). Our innovative analytics go beyond qualitative study, and includes user data collected from a relatively modern but increasingly ubiquitous technique of location tracking, with the goal of creating quantitative workflow metrics. Although we believe that the methods we have developed will generalize well to other settings, additional work will be required to demonstrate their broad utility beyond our single study environment. Highlights Clinical workflow analytics framework using Radio-Frequency Identification (RFID). Used the emergency department at the Mayo Clinic as a case study. Three phases of data flow: transformation, analysis, and visualization. Created quantitative techniques to complement traditional qualitative methods. Goal was to develop generalizable methods for critical care environments. Graphical abstract [DISPLAY OMISSION]

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  8. [해외논문]   A shared latent space matrix factorisation method for recommending new trial evidence for systematic review updates   SCI SCIE

    Surian, Didi (Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia ) , Dunn, Adam G. (Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia ) , Orenstein, Liat (Computational Health Informatics Program, Boston Children's Hospital, Boston, United States ) , Bashir, Rabia (Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia ) , Coiera, Enrico (Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia ) , Bourgeois, Florence T. (Computational Health Informatics Program, Boston Children's Hospital, Boston, United States)
    Journal of biomedical informatics v.79 ,pp. 32 - 40 , 2018 , 1532-0464 ,

    초록

    Abstract Background Clinical trial registries can be used to monitor the production of trial evidence and signal when systematic reviews become out of date. However, this use has been limited to date due to the extensive manual review required to search for and screen relevant trial registrations. Our aim was to evaluate a new method that could partially automate the identification of trial registrations that may be relevant for systematic review updates. Materials and methods We identified 179 systematic reviews of drug interventions for type 2 diabetes, which included 537 clinical trials that had registrations in ClinicalTrials.gov. Text from the trial registrations were used as features directly, or transformed using Latent Dirichlet Allocation (LDA) or Principal Component Analysis (PCA). We tested a novel matrix factorisation approach that uses a shared latent space to learn how to rank relevant trial registrations for each systematic review, comparing the performance to document similarity to rank relevant trial registrations. The two approaches were tested on a holdout set of the newest trials from the set of type 2 diabetes systematic reviews and an unseen set of 141 clinical trial registrations from 17 updated systematic reviews published in the Cochrane Database of Systematic Reviews. The performance was measured by the number of relevant registrations found after examining 100 candidates (recall@100) and the median rank of relevant registrations in the ranked candidate lists. Results The matrix factorisation approach outperformed the document similarity approach with a median rank of 59 (of 128,392 candidate registrations in ClinicalTrials.gov) and recall@100 of 60.9% using LDA feature representation, compared to a median rank of 138 and recall@100 of 42.8% in the document similarity baseline. In the second set of systematic reviews and their updates, the highest performing approach used document similarity and gave a median rank of 67 (recall@100 of 62.9%). Conclusions A shared latent space matrix factorisation method was useful for ranking trial registrations to reduce the manual workload associated with finding relevant trials for systematic review updates. The results suggest that the approach could be used as part of a semi-automated pipeline for monitoring potentially new evidence for inclusion in a review update. Highlights Trial registrations are underutilised in supporting systematic review updates. Matrix factorisation is used to rank new trials using trials from published reviews. Matrix factorisation and document similarity outperform manual search construction. The proposed method could replace searching and improve screening efficiency. The proposed method may complement methods applied to bibliographic databases. Graphical abstract [DISPLAY OMISSION]

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  9. [해외논문]   Exploration of association rule mining for coding consistency and completeness assessment in inpatient administrative health data   SCI SCIE

    Peng, Mingkai (Department of Community Health Sciences, University of Calgary, Calgary, Canada ) , Sundararajan, Vijaya (Department of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, Australia ) , Williamson, Tyler (Department of Community Health Sciences, University of Calgary, Calgary, Canada ) , Minty, Evan P. (Cumming School of Medicine, University of Calgary, Calgary, Canada ) , Smith, Tony C. (Department of Computer Science, University of Waikato, Hamilton, New Zealand ) , Doktorchik, Chelsea T.A. (Department of Community Health Sciences, University of Calgary, Calgary, Canada ) , Quan, Hude (Department of Community Health Sciences, University of Calgary, Calgary, Canada)
    Journal of biomedical informatics v.79 ,pp. 41 - 47 , 2018 , 1532-0464 ,

    초록

    Abstract Objective Data quality assessment is a challenging facet for research using coded administrative health data. Current assessment approaches are time and resource intensive. We explored whether association rule mining (ARM) can be used to develop rules for assessing data quality. Materials and methods We extracted 2013 and 2014 records from the hospital discharge abstract database (DAD) for patients between the ages of 55 and 65 from five acute care hospitals in Alberta, Canada. The ARM was conducted using the 2013 DAD to extract rules with support ≥0.0019 and confidence ≥0.5 using the bootstrap technique, and tested in the 2014 DAD. The rules were compared against the method of coding frequency and assessed for their ability to detect error introduced by two kinds of data manipulation: random permutation and random deletion. Results The association rules generally had clear clinical meanings. Comparing 2014 data to 2013 data (both original), there were 3 rules with a confidence difference >0.1, while coding frequency difference of codes in the right hand of rules was less than 0.004. After random permutation of 50% of codes in the 2014 data, average rule confidence dropped from 0.72 to 0.27 while coding frequency remained unchanged. Rule confidence decreased with the increase of coding deletion, as expected. Rule confidence was more sensitive to code deletion compared to coding frequency, with slope of change ranging from 1.7 to 184.9 with a median of 9.1. Conclusion The ARM is a promising technique to assess data quality. It offers a systematic way to derive coding association rules hidden in data, and potentially provides a sensitive and efficient method of assessing data quality compared to standard methods. Highlights Exploration of association rule mining for data quality rule development. The derived coding association rules had clearly clinical meanings. Rules can efficiently check coding consistency and completeness at high granularity. Graphical abstract [DISPLAY OMISSION]

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  10. [해외논문]   Mortality prediction in intensive care units (ICUs) using a deep rule-based fuzzy classifier   SCI SCIE

    Davoodi, Raheleh (Corresponding author at: Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.) , Moradi, Mohammad Hassan
    Journal of biomedical informatics v.79 ,pp. 48 - 59 , 2018 , 1532-0464 ,

    초록

    Abstract Electronic health records (EHRs) contain critical information useful for clinical studies. Early assessment of patients’ mortality in intensive care units is of great importance. In this paper, a Deep Rule-Based Fuzzy System (DRBFS) was proposed to develop an accurate in-hospital mortality prediction in the intensive care unit (ICU) patients employing a large number of input variables. Our main contribution is proposing a system, which is capable of dealing with big data with heterogeneous mixed categorical and numeric attributes. In DRBFS, the hidden layer in each unit is represented by interpretable fuzzy rules. Benefiting the strength of soft partitioning, a modified supervised fuzzy k-prototype clustering has been employed for fuzzy rule generation. According to the stacked approach, the same input space is kept in every base building unit of DRBFS. The training set in addition to random shifts, obtained from random projections of prediction results of the current base building unit is presented as the input of the next base building unit. A cohort of 10,972 adult admissions was selected from Medical Information Mart for Intensive Care (MIMIC-III) data set, where 9.31% of patients have died in the hospital. A heterogeneous feature set of first 48 h from ICU admissions, were extracted for in-hospital mortality rate. Required preprocessing and appropriate feature extraction were applied. To avoid biased assessments, performance indexes were calculated using holdout validation. We have evaluated our proposed method with several common classifiers including naIve Bayes (NB), decision trees (DT), Gradient Boosting (GB), Deep Belief Networks (DBN) and D-TSK-FC. The area under the receiver operating characteristics curve (AUROC) for NB, DT, GB, DBN, D-TSK-FC and our proposed method were 73.51%, 61.81%, 72.98%, 70.07%, 66.74% and 73.90% respectively. Our results have demonstrated that DRBFS outperforms various methods, while maintaining interpretable rule bases. Besides, benefiting from specific clustering methods, DRBFS can be well scaled up for large heterogeneous data sets. Highlights This study establishes a modified deep rule-based fuzzy model (DRBFS) in a novel cohort of MIMIC-III, where a combination of static and temporal information of variables are considered together. Supervised fuzzy clustering technique for training the antecedents of the rules which is designed to deal with mixed numeric and categorical attributes data set was employed. Stacked structure embedded in DRBFS, guarantees interpretability of rules in all layers. In DRBFS, to preserve interpretability, a supervised attribute-type specific random shift was employed in a stacked approach and the final decision is performed via fusion of the output of all layers. DRBFS is performed to predict the rate of the patient’s in-hospital mortality in the large database and the results demonstrated the superiority of DRBFS among various classifiers. Graphical abstract [DISPLAY OMISSION]

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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