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BMC Medical Informatics and Decision Making

Patience E Idoga, Mehmet Toycan, Halil Nadiri, Erbuğ Çelebi
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. This study, therefore, aim to examine, identify and evaluate the factors affecting healthcare professionals' intention to accept the cloud-based health center (CBHC) in developing countries...
February 19, 2019: BMC Medical Informatics and Decision Making
Andreas Philipp Hassler, Ernestina Menasalvas, Francisco José García-García, Leocadio Rodríguez-Mañas, Andreas Holzinger
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. The frailty syndrome is associated with a high risk for falls, hospitalization, disability, and finally increased mortality. Using predictive data mining enables the discovery of potential risk factors and can be used as clinical decision support system, which provides the medical doctor with information on the probable clinical patient outcome...
February 18, 2019: BMC Medical Informatics and Decision Making
Feichen Shen, Yiqing Zhao, Liwei Wang, Majid Rastegar Mojarad, Yanshan Wang, Sijia Liu, Hongfang Liu
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). METHODS: We first applied association rule mining algorithms on EMR to extract significant phenotype-disease associations and enriched existing rare disease resources (Human Phenotype Ontology and Orphanet (HPO-Orphanet))...
February 14, 2019: BMC Medical Informatics and Decision Making
Xia Jing, Matthew Emerson, David Masters, Matthew Brooks, Jacob Buskirk, Nasseef Abukamail, Chang Liu, James J Cimino, Jay Shubrook, Sonsoles De Lacalle, Yuchun Zhou, Vimla L Patel
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. Although graphic representations can help to augment human understanding of such data sets, a graph with hundreds or thousands of nodes challenges human comprehension. To improve comprehension, new tools are needed to extract the overviews of such data sets...
February 14, 2019: BMC Medical Informatics and Decision Making
Amanda L Terry, Moira Stewart, Sonny Cejic, J Neil Marshall, Simon de Lusignan, Bert M Chesworth, Vijaya Chevendra, Heather Maddocks, Joshua Shadd, Fred Burge, Amardeep Thind
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. We describe the process of developing and testing this set of measures, share the results of applying these measures in three EMR-derived datasets, and discuss what this reveals about the measures and EMR data quality...
February 12, 2019: BMC Medical Informatics and Decision Making
Charlotte Quintens, Thomas De Rijdt, Tine Van Nieuwenhuyse, Steven Simoens, Willy E Peetermans, Bart Van den Bosch, Minne Casteels, Isabel Spriet
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...
February 11, 2019: BMC Medical Informatics and Decision Making
Jose Cadena, David Falcone, Achla Marathe, Anil Vullikanti
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). School immunization data has limited demographic information-as a result, such analyses are not able to provide demographic characteristics of significant clusters...
February 4, 2019: BMC Medical Informatics and Decision Making
Haukur T Gudmundsson, Karen E Hansen, Bjarni V Halldorsson, Bjorn R Ludviksson, Bjorn Gudbjornsson
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. The present study compares the treatment recommendations provided by OPAD, an expert physician and the National Osteoporosis Guideline Group (NOGG). METHODS: We performed a retrospective analysis of 259 patients attending the outpatient osteoporosis clinic at the University Hospital in Iceland...
February 1, 2019: BMC Medical Informatics and Decision Making
Zengjian Liu, Xiaolong Wang, Qingcai Chen, Buzhou Tang, Hua Xu
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...
January 31, 2019: BMC Medical Informatics and Decision Making
Guocai Chen, Yuxi Jia, Lisha Zhu, Ping Li, Lin Zhang, Cui Tao, W Jim Zheng
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...
January 31, 2019: BMC Medical Informatics and Decision Making
Zhiheng Li, Zhihao Yang, Chen Shen, Jun Xu, Yaoyun Zhang, Hua Xu
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...
January 31, 2019: BMC Medical Informatics and Decision Making
Lindsay P Zimmerman, Paul A Reyfman, Angela D R Smith, Zexian Zeng, Abel Kho, L Nelson Sanchez-Pinto, Yuan Luo
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...
January 31, 2019: BMC Medical Informatics and Decision Making
Tianzhong Yang, Yang Yang, Yugang Jia, Xiao Li
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...
January 31, 2019: BMC Medical Informatics and Decision Making
Yaoyun Zhang, Cui Tao, Yang Gong, Kai Wang, Zhongming Zhao
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...
January 31, 2019: BMC Medical Informatics and Decision Making
Tian Mei, Xiaoyan Wei, Ziyi Chen, Xianghua Tian, Nan Dong, Dongmei Li, Yi Zhou
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...
January 31, 2019: BMC Medical Informatics and Decision Making
Domenik Muigg, Peter Kastner, Georg Duftschmid, Robert Modre-Osprian, Daniela Haluza
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. METHODS: We conducted an online survey among a purposive sample of Austrian practitioners (n = 41) using an adapted German version of the practitioner telehealth readiness assessment tool...
January 29, 2019: BMC Medical Informatics and Decision Making
Spyridon Kalogiannis, Konstantinos Deltouzos, Evangelia I Zacharaki, Andreas Vasilakis, Konstantinos Moustakas, John Ellul, Vasileios Megalooikonomou
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...
January 28, 2019: BMC Medical Informatics and Decision Making
John Bedson, Jonathon Hill, David White, Ying Chen, Simon Wathall, Stephen Dent, Kendra Cooke, Danielle van der Windt
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. Specifically, the study goal was to assess face, content and construct validity of data collection using the Pain Recorder in primary care patients receiving new analgesic prescriptions for musculoskeletal pain, as well as to assess its acceptability and clinical utility...
January 25, 2019: BMC Medical Informatics and Decision Making
Sebastian Potthoff, Justin Presseau, Falko F Sniehotta, Matthew Breckons, Amy Rylance, Leah Avery
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.g. habit and automaticity) and how the myriad of competing demands for their time may influence uptake...
January 24, 2019: BMC Medical Informatics and Decision Making
Grace K Dy, Mary K Nesline, Antonios Papanicolau-Sengos, Paul DePietro, Charles M LeVea, Amy Early, Hongbin Chen, Anne Grand'Maison, Patrick Boland, Marc S Ernstoff, Stephen Edge, Stacey Akers, Mateusz Opyrchal, Gurkamal Chatta, Kunle Odunsi, Sarabjot Pabla, Jeffrey M Conroy, Sean T Glenn, Hanchun T DeFedericis, Blake Burgher, Jonathan Andreas, Vincent Giamo, Maochun Qin, Yirong Wang, Kazunori Kanehira, Felicia L Lenzo, Peter Frederick, Shashikant Lele, Lorenzo Galluzzi, Boris Kuvshinoff, Carl Morrison
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...
January 18, 2019: BMC Medical Informatics and Decision Making
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