1,557 results match your criteria BMC Medical Informatics and Decision Making [Journal]


Assessing factors militating against the acceptance and successful implementation of a cloud based health center from the healthcare professionals' perspective: a survey of hospitals in Benue state, northcentral Nigeria.

BMC Med Inform Decis Mak 2019 Feb 19;19(1):34. Epub 2019 Feb 19.

Department of Computer Engineering, Cyprus International University, via Mersin 10, Nicosia, North-Cyprus, Turkey.

Background: Cloud based health platforms (CBHP) have tremendous capacity to meet patient's health needs. The benefits inherent in CBHP position it to be relevant for efficient healthcare delivery. Nonetheless, studies have shown that the adoption of new technologies is sometimes a challenge especially in developing nations. Read More

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http://dx.doi.org/10.1186/s12911-019-0751-xDOI Listing
February 2019

Importance of medical data preprocessing in predictive modeling and risk factor discovery for the frailty syndrome.

BMC Med Inform Decis Mak 2019 Feb 18;19(1):33. Epub 2019 Feb 18.

Holzinger Group, HCI-KDD, Institute for Medical Informatics/Statistics, Medical University Graz, Graz, 8036, Austria.

Background: Increasing life expectancy results in more elderly people struggling with age related diseases and functional conditions. This poses huge challenges towards establishing new approaches for maintaining health at a higher age. An important aspect for age related deterioration of the general patient condition is frailty. Read More

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http://dx.doi.org/10.1186/s12911-019-0747-6DOI Listing
February 2019

Rare disease knowledge enrichment through a data-driven approach.

BMC Med Inform Decis Mak 2019 Feb 14;19(1):32. Epub 2019 Feb 14.

Department of Health Sciences Research, Mayo Clinic, 205 3rd Ave SW, Rochester, MN, 55905, USA.

Background: Existing resources to assist the diagnosis of rare diseases are usually curated from the literature that can be limited for clinical use. It often takes substantial effort before the suspicion of a rare disease is even raised to utilize those resources. The primary goal of this study was to apply a data-driven approach to enrich existing rare disease resources by mining phenotype-disease associations from electronic medical record (EMR). Read More

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http://dx.doi.org/10.1186/s12911-019-0752-9DOI Listing
February 2019

A visual interactive analytic tool for filtering and summarizing large health data sets coded with hierarchical terminologies (VIADS).

BMC Med Inform Decis Mak 2019 Feb 14;19(1):31. Epub 2019 Feb 14.

The New York Academy of Medicine, New York, NY, USA.

Background: Vast volumes of data, coded through hierarchical terminologies (e.g., International Classification of Diseases, Tenth Revision-Clinical Modification [ICD10-CM], Medical Subject Headings [MeSH]), are generated routinely in electronic health record systems and medical literature databases. Read More

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http://dx.doi.org/10.1186/s12911-019-0750-yDOI Listing
February 2019

A basic model for assessing primary health care electronic medical record data quality.

BMC Med Inform Decis Mak 2019 Feb 12;19(1):30. Epub 2019 Feb 12.

Department of Family Medicine, Department of Epidemiology & Biostatistics, Schulich Interfaculty Program in Public Health, Schulich School of Medicine and Dentistry, The University of Western Ontario, 1151 Richmond Street, London, Ontario, N6A 3K7, Canada.

Background: The increased use of electronic medical records (EMRs) in Canadian primary health care practice has resulted in an expansion of the availability of EMR data. Potential users of these data need to understand their quality in relation to the uses to which they are applied. Herein, we propose a basic model for assessing primary health care EMR data quality, comprising a set of data quality measures within four domains. Read More

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http://dx.doi.org/10.1186/s12911-019-0740-0DOI Listing
February 2019

Development and implementation of "Check of Medication Appropriateness" (CMA): advanced pharmacotherapy-related clinical rules to support medication surveillance.

BMC Med Inform Decis Mak 2019 Feb 11;19(1):29. Epub 2019 Feb 11.

Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Herestraat 49, B-3000, Leuven, Belgium.

Background: To improve medication surveillance and provide pharmacotherapeutic support in University Hospitals Leuven, a back-office clinical service, called "Check of Medication Appropriateness" (CMA), was developed, consisting of clinical rule based screening for medication inappropriateness. The aim of this study is twofold: 1) describing the development of CMA and 2) evaluating the preliminary results, more specifically the number of clinical rule alerts, number of actions on the alerts and acceptance rate by physicians.

Methods: CMA focuses on patients at risk for potentially inappropriate medication and involves the daily checking by a pharmacist of high-risk prescriptions generated by advanced clinical rules integrating patient specific characteristics with details on medication. Read More

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http://dx.doi.org/10.1186/s12911-019-0748-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371500PMC
February 2019

Discovery of under immunized spatial clusters using network scan statistics.

BMC Med Inform Decis Mak 2019 Feb 4;19(1):28. Epub 2019 Feb 4.

Biocomplexity Institute & Initiative, University of Virginia, 995 Research Park Boulevard, Charlottesville, VA, 22911, USA.

Background: Clusters of under-vaccinated children are emerging in a number of states in the United States due to rising rates of vaccine hesitancy and refusal. As the measles outbreaks in California and other states in 2015 and in Minnesota in 2017 showed, such clusters can pose a significant public health risk. Prior methods have used publicly-available school immunization data for analysis (except for a few, which use private healthcare patient records). Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
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http://dx.doi.org/10.1186/s12911-018-0706-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6360755PMC
February 2019
2 Reads

Clinical decision support system for the management of osteoporosis compared to NOGG guidelines and an osteology specialist: a validation pilot study.

BMC Med Inform Decis Mak 2019 Feb 1;19(1):27. Epub 2019 Feb 1.

Landspitali - University Hospital, Reykjavik, Iceland.

Background: Although osteoporosis is an easily diagnosed and treatable condition, many individuals remain untreated. Clinical decision support systems might increase appropriate treatment of osteoporosis. We designed the Osteoporosis Advisor (OPAD), a computerized tool to support physicians managing osteoporosis at the point-of-care. Read More

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http://dx.doi.org/10.1186/s12911-019-0749-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6359836PMC
February 2019
1 Read

Temporal indexing of medical entity in Chinese clinical notes.

BMC Med Inform Decis Mak 2019 Jan 31;19(Suppl 1):17. Epub 2019 Jan 31.

School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.

Background: The goal of temporal indexing is to select an occurred time or time interval for each medical entity in clinical notes, so that all medical entities can be indexed on a united timeline, which could assist the understanding of clinical notes and the further application of medical entities. Some temporal relation shared tasks for the medical entity in English clinical notes have been organized in the past few years, such as the 2012 i2b2 NLP challenge, 2015 and 2016 clinical TempEval challenges. In these tasks, many heuristics rule-based and machine learning-based systems have been developed. Read More

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http://dx.doi.org/10.1186/s12911-019-0735-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6354334PMC
January 2019

Gene fingerprint model for literature based detection of the associations among complex diseases: a case study of COPD.

BMC Med Inform Decis Mak 2019 Jan 31;19(Suppl 1):20. Epub 2019 Jan 31.

School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin St Suite 600, Houston, TX, 77030, USA.

Background: Disease comorbidity is very common and has significant impact on disease treatment. Revealing the associations among diseases may help to understand the mechanisms of diseases, improve the prevention and treatment of diseases, and support the discovery of new drugs or new uses of existing drugs.

Methods: In this paper, we introduced a mathematical model to represent gene related diseases with a series of associated genes based on the overrepresentation of genes and diseases in PubMed literature. Read More

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http://dx.doi.org/10.1186/s12911-019-0738-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6354331PMC
January 2019
1 Read

Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text.

BMC Med Inform Decis Mak 2019 Jan 31;19(Suppl 1):22. Epub 2019 Jan 31.

School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.

Background: Extracting relations between important clinical entities is critical but very challenging for natural language processing (NLP) in the medical domain. Researchers have applied deep learning-based approaches to clinical relation extraction; but most of them consider sentence sequence only, without modeling syntactic structures. The aim of this study was to utilize a deep neural network to capture the syntactic features and further improve the performances of relation extraction in clinical notes. Read More

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http://dx.doi.org/10.1186/s12911-019-0736-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6354333PMC
January 2019

Early prediction of acute kidney injury following ICU admission using a multivariate panel of physiological measurements.

BMC Med Inform Decis Mak 2019 Jan 31;19(Suppl 1):16. Epub 2019 Jan 31.

Northwestern University, Evanston, IL, 60208, USA.

Background: The development of acute kidney injury (AKI) during an intensive care unit (ICU) admission is associated with increased morbidity and mortality.

Methods: Our objective was to develop and validate a data driven multivariable clinical predictive model for early detection of AKI among a large cohort of adult critical care patients. We utilized data form the Medical Information Mart for Intensive Care III (MIMIC-III) for all patients who had a creatinine measured for 3 days following ICU admission and excluded patients with pre-existing condition of Chronic Kidney Disease and Acute Kidney Injury on admission. Read More

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http://dx.doi.org/10.1186/s12911-019-0733-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6354330PMC
January 2019

Dynamic prediction of hospital admission with medical claim data.

BMC Med Inform Decis Mak 2019 Jan 31;19(Suppl 1):18. Epub 2019 Jan 31.

Philips Research North America, Cambridge, MA, 02141, USA.

Background: Congestive heart failure is one of the most common reasons those aged 65 and over are hospitalized in the United States, which has caused a considerable economic burden. The precise prediction of hospitalization caused by congestive heart failure in the near future could prevent possible hospitalization, optimize the medical resources, and better meet the healthcare needs of patients.

Methods: To fully utilize the monthly-updated claim feed data released by The Centers for Medicare and Medicaid Services (CMS), we present a dynamic random survival forest model adapted for periodically updated data to predict the risk of adverse events. Read More

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http://dx.doi.org/10.1186/s12911-019-0734-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6354329PMC
January 2019
1 Read

The International Conference on Intelligent Biology and Medicine 2018: Medical Informatics Thematic Track (MedicalInfo2018).

BMC Med Inform Decis Mak 2019 Jan 31;19(Suppl 1):21. Epub 2019 Jan 31.

Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.

In this editorial, we first summarize the 2018 International Conference on Intelligent Biology and Medicine (ICIBM 2018) that was held on June 10-12, 2018 in Los Angeles, California, USA, and then briefly introduce the six research articles included in this supplement issue. At ICIBM 2018, a special theme of Medical Informatics was dedicated to recent advances of data science in the medical domain. After peer review, six articles were selected in this thematic issue, covering topics such as clinical predictive modeling, clinical natural language processing (NLP), electroencephalogram (EEG) network analysis, and text mining in biomedical literature. Read More

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http://dx.doi.org/10.1186/s12911-019-0732-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6354328PMC
January 2019

Epileptic foci localization based on mapping the synchronization of dynamic brain network.

BMC Med Inform Decis Mak 2019 Jan 31;19(Suppl 1):19. Epub 2019 Jan 31.

Department of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.

Background: Characterizing the synchronous changes of epileptic seizures in different stages between different regions is profound to understand the transmission pathways of epileptic brain network and epileptogenic foci. There is currently no adequate quantitative calculation method for describing the propagation pathways of electroencephalogram (EEG) signals in the brain network from the short and long term. The goal of this study is to explore the innovative method to locate epileptic foci, mapping synchronization in the brain networks based on EEG. Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
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http://dx.doi.org/10.1186/s12911-019-0737-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6354332PMC
January 2019
2 Reads

Readiness to use telemonitoring in diabetes care: a cross-sectional study among Austrian practitioners.

BMC Med Inform Decis Mak 2019 Jan 29;19(1):26. Epub 2019 Jan 29.

Institute of Environmental Health, Center for Public Health, Medical University of Vienna, Vienna, Austria.

Background: Telemonitoring services could dramatically improve the care of diabetes patients by enhancing their quality of life while decreasing healthcare expenditures. However, the potential for implementing innovative treatment options in the Austrian public and private health system is not known yet. Thus, we analyzed the readiness to use telemonitoring in diabetes care among Austrian practitioners. Read More

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http://dx.doi.org/10.1186/s12911-019-0746-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6352347PMC
January 2019
1.496 Impact Factor

Integrating an openEHR-based personalized virtual model for the ageing population within HBase.

BMC Med Inform Decis Mak 2019 Jan 28;19(1):25. Epub 2019 Jan 28.

Computer Engineering and Informatics Department, University of Patras, University Campus, Rio, 26504, Greece.

Background: Frailty is a common clinical syndrome in ageing population that carries an increased risk for adverse health outcomes including falls, hospitalization, disability, and mortality. As these outcomes affect the health and social care planning, during the last years there is a tendency of investing in monitoring and preventing strategies. Although a number of electronic health record (EHR) systems have been developed, including personalized virtual patient models, there are limited ageing population oriented systems. Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
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http://dx.doi.org/10.1186/s12911-019-0745-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6350370PMC
January 2019
5 Reads

Development and validation of a pain monitoring app for patients with musculoskeletal conditions (The Keele pain recorder feasibility study).

BMC Med Inform Decis Mak 2019 Jan 25;19(1):24. Epub 2019 Jan 25.

Arthritis Research UK Primary Care Centre, Research Institute for Primary Care & Health Sciences, Keele University, Keele, Staffordshire, ST5 5BG, UK.

Background: Assessing daily change in pain and related symptoms help in diagnosis, prognosis, and monitoring response to treatment. However, such changes are infrequently assessed, and usually reviewed weeks or months after the start of treatment. We therefore developed a smartphone application (Keele Pain Recorder) to record information on the severity and impact of pain on daily life. Read More

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http://dx.doi.org/10.1186/s12911-019-0741-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6347830PMC
January 2019
4 Reads

Exploring the role of competing demands and routines during the implementation of a self-management tool for type 2 diabetes: a theory-based qualitative interview study.

BMC Med Inform Decis Mak 2019 Jan 24;19(1):23. Epub 2019 Jan 24.

School of Health & Social Care, Teesside University, Middlesbrough, TS1 3BA, UK.

Background: The implementation of new medical interventions into routine care involves healthcare professionals adopting new clinical behaviours and changing existing ones. Whilst theory-based approaches can help understand healthcare professionals' behaviours, such approaches often focus on a single behaviour and conceptualise its performance in terms of an underlying reflective process. Such approaches fail to consider the impact of non-reflective influences (e. Read More

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http://dx.doi.org/10.1186/s12911-019-0744-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6345053PMC
January 2019
1 Read

Treatment recommendations to cancer patients in the context of FDA guidance for next generation sequencing.

BMC Med Inform Decis Mak 2019 Jan 18;19(1):14. Epub 2019 Jan 18.

OmniSeq, Inc., Buffalo, NY, 14203, USA.

Background: Regulatory approval of next generation sequencing (NGS) by the FDA is advancing the use of genomic-based precision medicine for the therapeutic management of cancer as standard care. Recent FDA guidance for the classification of genomic variants based on clinical evidence to aid clinicians in understanding the actionability of identified variants provided by comprehensive NGS panels has also been set forth. In this retrospective analysis, we interpreted and applied the FDA variant classification guidance to comprehensive NGS testing performed for advanced cancer patients and assessed oncologist agreement with NGS test treatment recommendations. Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
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http://dx.doi.org/10.1186/s12911-019-0743-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6339275PMC
January 2019
6 Reads

Replicating medication trend studies using ad hoc information extraction in a clinical data warehouse.

BMC Med Inform Decis Mak 2019 Jan 18;19(1):15. Epub 2019 Jan 18.

Computer Science, Unviversity of Würzburg, Am Hubland, Würzburg, 97074, Germany.

Background: Medication trend studies show the changes of medication over the years and may be replicated using a clinical Data Warehouse (CDW). Even nowadays, a lot of the patient information, like medication data, in the EHR is stored in the format of free text. As the conventional approach of information extraction (IE) demands a high developmental effort, we used ad hoc IE instead. Read More

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http://dx.doi.org/10.1186/s12911-018-0729-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6339317PMC
January 2019
2 Reads

Ultra-short term HRV features as surrogates of short term HRV: a case study on mental stress detection in real life.

BMC Med Inform Decis Mak 2019 Jan 17;19(1):12. Epub 2019 Jan 17.

School of Engineering, University of Warwick, CV47AL, Coventry, UK.

Background: This paper suggests a method to assess the extent to which ultra-short Heart Rate Variability (HRV) features (less than 5 min) can be considered as valid surrogates of short HRV features (nominally 5 min). Short term HRV analysis has been widely investigated for mental stress assessment, whereas the validity of ultra-short HRV features remains unclear. Therefore, this study proposes a method to explore the extent to which HRV excerpts can be shortened without losing their ability to automatically detect mental stress. Read More

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http://dx.doi.org/10.1186/s12911-019-0742-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6335694PMC
January 2019
1 Read

Modelling cancer outcomes of bone metastatic patients: combining survival data with N-Telopeptide of type I collagen (NTX) dynamics through joint models.

BMC Med Inform Decis Mak 2019 Jan 17;19(1):13. Epub 2019 Jan 17.

INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Rua Alves Redol, 9, Lisboa, 1000-029, Portugal.

Background: Joint models (JM) have emerged as a promising statistical framework to concurrently analyse survival data and multiple longitudinal responses. This is particularly relevant in clinical studies where the goal is to estimate the association between time-to-event data and the biomarkers evolution. In the context of oncological data, JM can indeed provide interesting prognostic markers for the event under study and thus support clinical decisions and treatment choices. Read More

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http://dx.doi.org/10.1186/s12911-018-0728-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6337820PMC
January 2019
1 Read
1.496 Impact Factor

Development process of a mobile electronic medical record for nurses: a single case study.

BMC Med Inform Decis Mak 2019 Jan 14;19(1):11. Epub 2019 Jan 14.

Julius Center, University Medical Center Utrecht, P.O. Box 85500, 3508 GA, Utrecht, The Netherlands.

Background: With the growing shortage of nurses, labor-saving technology has become more important. In health care practice, however, the fit with innovations is not easy. The aim of this study is to analyze the development of a mobile input device for electronic medical records (MEMR), a potentially labor-saving application supported by nurses, that failed to meet the needs of nurses after development. Read More

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http://dx.doi.org/10.1186/s12911-018-0726-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6332569PMC
January 2019
1 Read

The state of research on cyberattacks against hospitals and available best practice recommendations: a scoping review.

BMC Med Inform Decis Mak 2019 Jan 11;19(1):10. Epub 2019 Jan 11.

Institute of Global Health, Faculty of Medicine, University of Geneva, Campus Biotech, Chemin des Mines 9, 1202, Geneva, Switzerland.

Background: The health sector has quickly become a target for cyberattacks. Hospitals are especially sensitive to these sorts of attacks as any disruption in operations or even disclosure of patient personal information can have far-reaching consequences. The objective of this study was to map the available literature on cyberattacks on hospitals and to identify the different domains of research, while extracting the recommendations and guidelines put forth in the literature. Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
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http://dx.doi.org/10.1186/s12911-018-0724-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6330387PMC
January 2019
6 Reads

Effectiveness of a decision aid for promoting colorectal cancer screening in Spain: a randomized trial.

BMC Med Inform Decis Mak 2019 Jan 10;19(1). Epub 2019 Jan 10.

Evaluation Unit of the Canary Islands Health Service (SESCS), s/n. 38109. El Rosario. S/C de Tenerife, Tenerife, Spain.

Background: Colorectal cancer (CRC) screening has shown to reduce incidence and mortality rates, and therefore is widely recommended for people above 50 years-old. However, despite the implementation of population-based screening programs in several countries, uptake rates are still low. Decision aids (DAs) may help patients to make informed decisions about CRC screening. Read More

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http://dx.doi.org/10.1186/s12911-019-0739-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6327535PMC
January 2019
1 Read

A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records.

BMC Med Inform Decis Mak 2019 Jan 10;19(1). Epub 2019 Jan 10.

Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden.

Background: Adverse drug events (ADEs) as well as other preventable adverse events in the hospital setting incur a yearly monetary cost of approximately $3.5 billion, in the United States alone. Therefore, it is of paramount importance to reduce the impact and prevalence of ADEs within the healthcare sector, not only since it will result in reducing human suffering, but also as a means to substantially reduce economical strains on the healthcare system. Read More

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http://dx.doi.org/10.1186/s12911-018-0717-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6327495PMC
January 2019
1 Read

Initial development of Supportive care Assessment, Prioritization and Recommendations for Kids (SPARK), a symptom screening and management application.

BMC Med Inform Decis Mak 2019 Jan 10;19(1). Epub 2019 Jan 10.

Division of Haematology/Oncology, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario, M5G 1X8, Canada.

Background: We developed Supportive care Prioritization, Assessment and Recommendations for Kids (SPARK), a web-based application designed to facilitate symptom screening by children receiving cancer treatments and access to supportive care clinical practice guidelines primarily by healthcare providers. The objective was to describe the initial development and evaluation of SPARK from the perspective of children.

Implementation: Development and evaluation occurred in three phases: (1) low fidelity focused on functionality, (2) design focused on "look and feel" and (3) high fidelity confirmed functionality and design. Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
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http://dx.doi.org/10.1186/s12911-018-0715-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6327501PMC
January 2019
6 Reads

Assigning value to preparation for prostate cancer decision making: a willingness to pay analysis.

BMC Med Inform Decis Mak 2019 Jan 9;19(1). Epub 2019 Jan 9.

Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA.

Background: The Personal Patient Profile-Prostate (P3P) is a web-based decision support system for men newly diagnosed with localized prostate cancer that has demonstrated efficacy in reducing decisional conflict. Our objective was to estimate willingness-to-pay (WTP) for men's decisional preparation activities.

Methods: In a multicenter, randomized trial of P3P, usual care group participants received typical preparation for decision making plus referral to publicly-available, educational websites. Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
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http://dx.doi.org/10.1186/s12911-018-0725-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6327504PMC
January 2019
5 Reads

A usability design checklist for Mobile electronic data capturing forms: the validation process.

BMC Med Inform Decis Mak 2019 Jan 9;19(1). Epub 2019 Jan 9.

Department of Information and Media Studies, University of Bergen, Bergen, Norway.

Background: New Specific Application Domain (SAD) heuristics or design principles are being developed to guide the design and evaluation of mobile applications in a bid to improve on the usability of these applications. This is because the existing heuristics are rather generic and are often unable to reveal a large number of mobile usability issues related to mobile specific interfaces and characteristics. Mobile Electronic Data Capturing Forms (MEDCFs) are one of such applications that are being used to collect health data particularly in hard to reach areas, but with a number of usability challenges especially when used in rural areas by semi literate users. Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
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http://dx.doi.org/10.1186/s12911-018-0718-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6325798PMC
January 2019
4 Reads

Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome.

BMC Med Inform Decis Mak 2019 Jan 9;19(1). Epub 2019 Jan 9.

College of Biomedical Engineering and Instrument Science, Zhejiang University, Key Lab for Biomedical Engineering of Ministry of Education, Zheda Road, Hangzhou, China.

Background: Main adverse cardiac events (MACE) are essentially composite endpoints for assessing safety and efficacy of treatment processes of acute coronary syndrome (ACS) patients. Timely prediction of MACE is highly valuable for improving the effects of ACS treatments. Most existing tools are specific to predict MACE by mainly using static patient features and neglecting dynamic treatment information during learning. Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
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http://dx.doi.org/10.1186/s12911-018-0730-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6325718PMC
January 2019
6 Reads

Predicting patient-reported outcomes following hip and knee replacement surgery using supervised machine learning.

BMC Med Inform Decis Mak 2019 Jan 8;19(1). Epub 2019 Jan 8.

German Research Center for Environmental Health, Institute for Health Economics and Health Care Management, Helmholtz Zentrum München, Postfach 1129, 85758, Neuherberg, Germany.

Background: Machine-learning classifiers mostly offer good predictive performance and are increasingly used to support shared decision-making in clinical practice. Focusing on performance and practicability, this study evaluates prediction of patient-reported outcomes (PROs) by eight supervised classifiers including a linear model, following hip and knee replacement surgery.

Methods: NHS PRO data (130,945 observations) from April 2015 to April 2017 were used to train and test eight classifiers to predict binary postoperative improvement based on minimal important differences. Read More

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http://dx.doi.org/10.1186/s12911-018-0731-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6325823PMC
January 2019
1 Read

Meta-analysis of predictive models to assess the clinical validity and utility for patient-centered medical decision making: application to the CAncer of the Prostate Risk Assessment (CAPRA).

BMC Med Inform Decis Mak 2019 Jan 7;19(1). Epub 2019 Jan 7.

SPHERE (methodS in Patient-centered outcomes & HEalth ResEarch) U1246, INSERM, Nantes University, Tours University, Nantes, France.

Background: The Cancer of the Prostate Risk Assessment (CAPRA) score was designed and validated several times to predict the biochemical recurrence-free survival after a radical prostatectomy. Our objectives were, first, to study the clinical validity of the CAPRA score, and, second, to assess its clinical utility for stratified medicine from an original patient-centered approach.

Methods: We proposed a meta-analysis based on a literature search using MEDLINE. Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
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http://dx.doi.org/10.1186/s12911-018-0727-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323757PMC
January 2019
5 Reads

A clinical text classification paradigm using weak supervision and deep representation.

BMC Med Inform Decis Mak 2019 Jan 7;19(1). Epub 2019 Jan 7.

Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 1st ST SW, Rochester, MN, 55905, USA.

Background: Automatic clinical text classification is a natural language processing (NLP) technology that unlocks information embedded in clinical narratives. Machine learning approaches have been shown to be effective for clinical text classification tasks. However, a successful machine learning model usually requires extensive human efforts to create labeled training data and conduct feature engineering. Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
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http://dx.doi.org/10.1186/s12911-018-0723-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6322223PMC
January 2019
4 Reads

Big data hurdles in precision medicine and precision public health.

BMC Med Inform Decis Mak 2018 12 29;18(1):139. Epub 2018 Dec 29.

Center for Health Outcomes and Informatics Research, Loyola University Chicago, Maywood, IL, 60153, USA.

Background: Nowadays, trendy research in biomedical sciences juxtaposes the term 'precision' to medicine and public health with companion words like big data, data science, and deep learning. Technological advancements permit the collection and merging of large heterogeneous datasets from different sources, from genome sequences to social media posts or from electronic health records to wearables. Additionally, complex algorithms supported by high-performance computing allow one to transform these large datasets into knowledge. Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
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http://dx.doi.org/10.1186/s12911-018-0719-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311005PMC
December 2018
6 Reads

Characterizing patient compliance over six months in remote digital trials of Parkinson's and Huntington disease.

BMC Med Inform Decis Mak 2018 12 20;18(1):138. Epub 2018 Dec 20.

Teva Pharmaceuticals International GmbH, Elisabethenstrasse 15, 4051, Basel, Switzerland.

Background: A growing number of clinical trials use various sensors and smartphone applications to collect data outside of the clinic or hospital, raising the question to what extent patients comply with the unique requirements of remote study protocols. Compliance is particularly important in conditions where patients are motorically and cognitively impaired. Here, we sought to understand patient compliance in digital trials of two such pathologies, Parkinson's disease (PD) and Huntington disease (HD). Read More

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http://dx.doi.org/10.1186/s12911-018-0714-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302308PMC
December 2018
12 Reads

Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer's disease: a feature selection ensemble combining stability and predictability.

BMC Med Inform Decis Mak 2018 12 19;18(1):137. Epub 2018 Dec 19.

LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal.

Background: Predicting progression from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) is an utmost open issue in AD-related research. Neuropsychological assessment has proven to be useful in identifying MCI patients who are likely to convert to dementia. However, the large battery of neuropsychological tests (NPTs) performed in clinical practice and the limited number of training examples are challenge to machine learning when learning prognostic models. Read More

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http://dx.doi.org/10.1186/s12911-018-0710-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6299964PMC
December 2018
1 Read

Promoting exercise training and physical activity in daily life: a feasibility study of a virtual group intervention for behaviour change in COPD.

BMC Med Inform Decis Mak 2018 12 18;18(1):136. Epub 2018 Dec 18.

University Hospital of North Norway, P.O. Box 35, N-9038, Tromsø, Norway.

Background: Physical inactivity is associated with poor health outcomes in chronic obstructive pulmonary disease (COPD). It is therefore crucial for patients to have a physically active lifestyle. The aims of this feasibility study were to assess a tablet-based physical activity behavioural intervention in virtual groups for COPD regarding 1) patients' acceptance 2) technology usability 3) patients' exercise programme adherence and 4) changes in patients' physical activity level. Read More

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http://dx.doi.org/10.1186/s12911-018-0721-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6299608PMC
December 2018
1 Read

Continuance compliance of privacy policy of electronic medical records: the roles of both motivation and habit.

BMC Med Inform Decis Mak 2018 12 18;18(1):135. Epub 2018 Dec 18.

Department of Family Medicine, E-Da Hospital, Kaohsiung City, Taiwan, Republic of China.

Background: Hospitals have increasingly realized that wholesale adoption of electronic medical records (EMR) may introduce differential tangible/intangible benefits to them, including improved quality-of-care, reduced medical errors, reduced costs, and allowable instant access to relevant patient information by healthcare professionals without the limitations of time/space. However, an increased reliance on EMR has also led to a corresponding increase in the negative impact exerted via EMR breaches possibly leading to unexpected damage for both hospitals and patients. This study investigated the possible antecedents that will influence hospital employees' continuance compliance with privacy policy of Electronic Medical Records (EMR). Read More

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http://dx.doi.org/10.1186/s12911-018-0722-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6299633PMC
December 2018
1 Read

Logistics management information system performance for program drugs in public health facilities of East Wollega Zone, Oromia regional state, Ethiopia.

BMC Med Inform Decis Mak 2018 12 17;18(1):133. Epub 2018 Dec 17.

Pharmaceutical supply chain management specialist, School of Pharmacy, Faculty of Health Sciences, Jimma University, Jimma, Ethiopia.

Background: Proper logistics management information system in the supply chain improves health outcomes by maintaining accurate and timely information. The purpose of this study was to determine program drugs logistics management information system performance in public health facilities of East Wollega Zone, Oromia Regional State.

Methods: A facility-based descriptive cross-sectional study design complemented with a qualitative method was conducted from April 01 to May 30, 2017. Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
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http://dx.doi.org/10.1186/s12911-018-0720-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6296105PMC
December 2018
9 Reads

Pilot study of decision support tools on breast cancer chemoprevention for high-risk women and healthcare providers in the primary care setting.

BMC Med Inform Decis Mak 2018 12 17;18(1):134. Epub 2018 Dec 17.

Columbia University, Mailman School of Public Health, 622 West 168th Street, PH-20, New York, NY, 10032, USA.

Background: Breast cancer chemoprevention can reduce breast cancer incidence in high-risk women; however, chemoprevention is underutilized in the primary care setting. We conducted a pilot study of decision support tools among high-risk women and their primary care providers (PCPs).

Methods: The intervention included a decision aid (DA) for high-risk women, RealRisks, and a provider-centered tool, Breast Cancer Risk Navigation (BNAV). Read More

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http://dx.doi.org/10.1186/s12911-018-0716-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6296071PMC
December 2018
1 Read

Inventory of oncologists' unmet needs for tools to support decision-making about palliative treatment for metastatic colorectal cancer.

BMC Med Inform Decis Mak 2018 12 14;18(1):132. Epub 2018 Dec 14.

Department of Epidemiology and Biostatistics, Amsterdam UMC, location VUMC, F-wing Medical Faculty building, PO Box 7057 1007, MB, Amsterdam, The Netherlands.

Background: Decision-making about palliative care for metastatic colorectal cancer (mCRC) consists of many different treatment-related decisions, and there generally is no best treatment option. Decision support systems (DSS), e.g. Read More

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http://dx.doi.org/10.1186/s12911-018-0712-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6295030PMC
December 2018
2 Reads

Decision makers' experience of participatory dynamic simulation modelling: methods for public health policy.

BMC Med Inform Decis Mak 2018 12 12;18(1):131. Epub 2018 Dec 12.

The Australian Prevention Partnership Centre, Sax Institute, PO Box K617, Haymarket, Sydney, NSW, 1240, Australia.

Background: Systems science methods such as dynamic simulation modelling are well suited to address questions about public health policy as they consider the complexity, context and dynamic nature of system-wide behaviours. Advances in technology have led to increased accessibility and interest in systems methods to address complex health policy issues. However, the involvement of policy decision makers in health-related simulation model development has been lacking. Read More

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http://dx.doi.org/10.1186/s12911-018-0707-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6291959PMC
December 2018
2 Reads

Improving palliative care with deep learning.

BMC Med Inform Decis Mak 2018 12 12;18(Suppl 4):122. Epub 2018 Dec 12.

Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.

Background: Access to palliative care is a key quality metric which most healthcare organizations strive to improve. The primary challenges to increasing palliative care access are a combination of physicians over-estimating patient prognoses, and a shortage of palliative staff in general. This, in combination with treatment inertia can result in a mismatch between patient wishes, and their actual care towards the end of life. Read More

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http://dx.doi.org/10.1186/s12911-018-0677-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6290509PMC
December 2018
3 Reads

EHR phenotyping via jointly embedding medical concepts and words into a unified vector space.

BMC Med Inform Decis Mak 2018 12 12;18(Suppl 4):123. Epub 2018 Dec 12.

Department of Computer & Information Sciences, Temple University, Philadelphia, PA, USA.

Background: There has been an increasing interest in learning low-dimensional vector representations of medical concepts from Electronic Health Records (EHRs). Vector representations of medical concepts facilitate exploratory analysis and predictive modeling of EHR data to gain insights about the patterns of care and health outcomes. EHRs contain structured data such as diagnostic codes and laboratory tests, as well as unstructured free text data in form of clinical notes, which provide more detail about condition and treatment of patients. Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
Publisher Site
http://dx.doi.org/10.1186/s12911-018-0672-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6290514PMC
December 2018
10 Reads

Chronic Kidney Disease stratification using office visit records: Handling data imbalance via hierarchical meta-classification.

BMC Med Inform Decis Mak 2018 12 12;18(Suppl 4):125. Epub 2018 Dec 12.

Computational Biomedicine Lab, Computer and Information Sciences, University of Delaware, Newark, DE, USA.

Background: Chronic Kidney Disease (CKD) is one of several conditions that affect a growing percentage of the US population; the disease is accompanied by multiple co-morbidities, and is hard to diagnose in-and-of itself. In its advanced forms it carries severe outcomes and can lead to death. It is thus important to detect the disease as early as possible, which can help devise effective intervention and treatment plan. Read More

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http://dx.doi.org/10.1186/s12911-018-0675-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6290512PMC
December 2018
1 Read

Evaluating lexical similarity and modeling discrepancies in the procedure hierarchy of SNOMED CT.

Authors:
Ankur Agrawal

BMC Med Inform Decis Mak 2018 12 12;18(Suppl 4):88. Epub 2018 Dec 12.

Department of Computer Science, Manhattan College, New York, NY, USA.

Background: SNOMED CT is a standardized and comprehensive clinical terminology that is used in Electronic Health Records to capture, store and access clinical data of patients. Studies have, however, shown that there are inconsistencies inherent in the modeling of concepts in SNOMED CT that can have an impact on its usage to record clinical data and in clinical decision-making tools.

Methods: An effective lexical approach to identifying inconsistencies with high likelihood in the structural modeling of the concepts of SNOMED CT is discussed and assessed. Read More

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http://dx.doi.org/10.1186/s12911-018-0673-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6290591PMC
December 2018
1 Read

Wrist accelerometer shape feature derivation methods for assessing activities of daily living.

BMC Med Inform Decis Mak 2018 12 12;18(Suppl 4):124. Epub 2018 Dec 12.

Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA.

Background: There has been an increasing interest in understanding the usefulness of wrist-based accelerometer data for physical activity (PA) assessment due to the ease of use and higher user compliance than other body placements. PA assessment studies have relied on machine learning methods which take accelerometer data in forms of variables, or feature vectors.

Methods: In this work, we introduce automated shape feature derivation methods to transform epochs of accelerometer data into feature vectors. Read More

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http://dx.doi.org/10.1186/s12911-018-0671-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6290590PMC
December 2018
2 Reads

Leveraging auxiliary measures: a deep multi-task neural network for predictive modeling in clinical research.

BMC Med Inform Decis Mak 2018 12 12;18(Suppl 4):126. Epub 2018 Dec 12.

Department of Emergency Medicine, Wayne State University, Detroit, MI, USA.

Background: Accurate predictive modeling in clinical research enables effective early intervention that patients are most likely to benefit from. However, due to the complex biological nature of disease progression, capturing the highly non-linear information from low-level input features is quite challenging. This requires predictive models with high-capacity. Read More

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http://dx.doi.org/10.1186/s12911-018-0676-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6290511PMC
December 2018
1 Read

A comparative quantitative study of utilizing artificial intelligence on electronic health records in the USA and China during 2008-2017.

BMC Med Inform Decis Mak 2018 12 7;18(Suppl 5):117. Epub 2018 Dec 7.

School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China.

Background: The application of artificial intelligence techniques for processing electronic health records data plays increasingly significant role in advancing clinical decision support. This study conducts a quantitative comparison on the research of utilizing artificial intelligence on electronic health records between the USA and China to discovery their research similarities and differences.

Methods: Publications from both Web of Science and PubMed are retrieved to explore the research status and academic performances of the two countries quantitatively. Read More

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http://dx.doi.org/10.1186/s12911-018-0692-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6284279PMC
December 2018
4 Reads
1.496 Impact Factor