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BMC Medical Informatics and Decision Making

Xavier Rafael-Palou, Cecilia Turino, Alexander Steblin, Manuel Sánchez-de-la-Torre, Ferran Barbé, Eloisa Vargiu
BACKGROUND: Patients suffering obstructive sleep apnea are mainly treated with continuous positive airway pressure (CPAP). Although it is a highly effective treatment, compliance with this therapy is problematic to achieve with serious consequences for the patients' health. Unfortunately, there is a clear lack of clinical analytical tools to support the early prediction of compliant patients. METHODS: This work intends to take a further step in this direction by building compliance classifiers with CPAP therapy at three different moments of the patient follow-up, before the therapy starts (baseline) and at months 1 and 3 after the baseline...
September 18, 2018: BMC Medical Informatics and Decision Making
Sooyoung Yoo, Seok Kim, Eunhye Kim, Eunja Jung, Kee-Hyuck Lee, Hee Hwang
BACKGROUND: Numerous hospitals and organizations have recently endeavored to study the effects of real-time location systems. However, their experiences of system adoption or pilot testing via implementation were not shared with others or evaluated in a real environment. Therefore, we aimed to share our experiences and insight regarding a real-time location system, obtained via the implementation and operation of a real-time asset tracking system based on Bluetooth Low Energy/WiFi in a tertiary care hospital, which can be used to improve hospital efficiency and nursing workflow...
September 10, 2018: BMC Medical Informatics and Decision Making
Kuteesa R Bisaso, Susan A Karungi, Agnes Kiragga, Jackson K Mukonzo, Barbara Castelnuovo
BACKGROUND: Treatment with effective antiretroviral therapy (ART) lowers morbidity and mortality among HIV positive individuals. Effective highly active antiretroviral therapy (HAART) should lead to undetectable viral load within 6 months of initiation of therapy. Failure to achieve and maintain viral suppression may lead to development of resistance and increase the risk of viral transmission. In this paper three logistic regression based machine learning approaches are developed to predict early virological outcomes using easily measurable baseline demographic and clinical variables (age, body weight, sex, TB disease status, ART regimen, viral load, CD4 count)...
September 4, 2018: BMC Medical Informatics and Decision Making
Rahul Krishnan Pathinarupothi, P Durga, Ekanath Srihari Rangan
BACKGROUND: With connected medical devices fast becoming ubiquitous in healthcare monitoring there is a deluge of data coming from multiple body-attached sensors. Transforming this flood of data into effective and efficient diagnosis is a major challenge. METHODS: To address this challenge, we present a 3P approach: personalized patient monitoring, precision diagnostics, and preventive criticality alerts. In a collaborative work with doctors, we present the design, development, and testing of a healthcare data analytics and communication framework that we call RASPRO (Rapid Active Summarization for effective PROgnosis)...
September 4, 2018: BMC Medical Informatics and Decision Making
Shaker El-Sappagh, Francesco Franda, Farman Ali, Kyung-Sup Kwak
BACKGROUND: Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT, hereafter abbreviated SCT) is a comprehensive medical terminology used for standardizing the storage, retrieval, and exchange of electronic health data. Some efforts have been made to capture the contents of SCT as Web Ontology Language (OWL), but these efforts have been hampered by the size and complexity of SCT. METHOD: Our proposal here is to develop an upper-level ontology and to use it as the basis for defining the terms in SCT in a way that will support quality assurance of SCT, for example, by allowing consistency checks of definitions and the identification and elimination of redundancies in the SCT vocabulary...
August 31, 2018: BMC Medical Informatics and Decision Making
Lingtong Min, Qi Tian, Xudong Lu, Huilong Duan
BACKGROUND: The openEHR approach can improve the interoperability of electronic health record (EHR) through two-level modeling. Developing archetypes for the complete EHR dataset is essential for implementing a large-scale interoperable EHR system with the openEHR approach. Although the openEHR approach has been applied in different domains, the feasibility of archetyping a complete EHR dataset in a hospital has not been reported in academic literature, especially in a country where using openEHR is still in its infancy stage, like China...
August 29, 2018: BMC Medical Informatics and Decision Making
Zhiwei Chen, Zhe He, Xiuwen Liu, Jiang Bian
After publication of this supplement article [1], it was brought to our attention that the Results section of the abstract contained a partial sentence.
August 22, 2018: BMC Medical Informatics and Decision Making
Zhaoyi Chen, Victoria Y Bird, Rupam Ruchi, Mark S Segal, Jiang Bian, Saeed R Khan, Marie-Carmelle Elie, Mattia Prosperi
BACKGROUND: Kidney stone (KS) disease has high, increasing prevalence in the United States and poses a massive economic burden. Diagnostics algorithms of KS only use a few variables with a limited sensitivity and specificity. In this study, we tested a big data approach to infer and validate a 'multi-domain' personalized diagnostic acute care algorithm for KS (DACA-KS), merging demographic, vital signs, clinical, and laboratory information. METHODS: We utilized a large, single-center database of patients admitted to acute care units in a large tertiary care hospital...
August 17, 2018: BMC Medical Informatics and Decision Making
Iona Crumley, Lisa Blom, Lucie Laflamme, Helle Mölsted Alvesson
BACKGROUND: Traumatic injury is a serious global health burden, particularly in low- and middle-income countries where medical care often lacks resources and expertise. In these contexts, diagnostic telemedicine could prove a cost effective tool, yet it remains largely underused here, and knowledge on its potential impact is limited. Particularly scarce is the view of the expert user physicians, and how they themselves relate to this technology. METHODS: This qualitative study investigated tele-experts' (n = 15) views on the potential for image based teleconsultation to be integrated in trauma and emergency care services...
August 1, 2018: BMC Medical Informatics and Decision Making
Duaa Aljabri, Adrian Dumitrascu, M Caroline Burton, Launia White, Mahmud Khan, Sudha Xirasagar, Ronnie Horner, James Naessens
BACKGROUND: Portal use has been studied among outpatients, but its utility and impact on inpatients is unclear. This study describes portal adoption and use among hospitalized cancer patients and investigates associations with selected safety, utilization, and satisfaction measures. METHODS: A retrospective review of 4594 adult hospitalized cancer patients was conducted between 2012 and 2014 at Mayo Clinic in Jacksonville, Florida, comparing portal adopters, who registered for a portal account prior to hospitalization, with nonadopters...
July 27, 2018: BMC Medical Informatics and Decision Making
Romaric Marcilly, Elske Ammenwerth, Erin Roehrer, Julie Niès, Marie-Catherine Beuscart-Zéphir
BACKGROUND: Usability flaws in medication alerting systems may have a negative impact on clinical use and patient safety. In order to prevent the release of alerting systems that contain such flaws, it is necessary to provide designers and evaluators with evidence-based usability design principles. The objective of the present study was to develop a comprehensive, structured list of evidence-based usability design principles for medication alerting systems. METHODS: Nine sets of design principles for medication alerting systems were analyzed, summarized, and structured...
July 24, 2018: BMC Medical Informatics and Decision Making
Jingcheng Du, Yaoyun Zhang, Jianhong Luo, Yuxi Jia, Qiang Wei, Cui Tao, Hua Xu
BACKGROUND: Suicide has been one of the leading causes of deaths in the United States. One major cause of suicide is psychiatric stressors. The detection of psychiatric stressors in an at risk population will facilitate the early prevention of suicidal behaviors and suicide. In recent years, the widespread popularity and real-time information sharing flow of social media allow potential early intervention in a large-scale population. However, few automated approaches have been proposed to extract psychiatric stressors from Twitter...
July 23, 2018: BMC Medical Informatics and Decision Making
Hansi Zhang, Yi Guo, Qian Li, Thomas J George, Elizabeth Shenkman, François Modave, Jiang Bian
BACKGROUND: Cancer is the second leading cause of death in the United States, exceeded only by heart disease. Extant cancer survival analyses have primarily focused on individual-level factors due to limited data availability from a single data source. There is a need to integrate data from different sources to simultaneously study as much risk factors as possible. Thus, we proposed an ontology-based approach to integrate heterogeneous datasets addressing key data integration challenges...
July 23, 2018: BMC Medical Informatics and Decision Making
Huiqin Chen, Dihua Zhang, Guoping Zhang, Xiaofeng Li, Ying Liang, Mohan Vamsi Kasukurthi, Shengyu Li, Glen M Borchert, Jingshan Huang
BACKGROUND: Acute lymphoblastic leukemia is the most prevalent neoplasia among children. Despite the tremendous achievements of state-of-the-art treatment strategies, drug resistance is still a major cause of chemotherapy failure leading to relapse in pediatric acute lymphoblastic leukemia. The underlying mechanisms of such phenomenon are not yet clear and subject to further exploration. Prior research has shown that microRNAs can act as post-transcriptional regulators of many genes related to drug resistance...
July 23, 2018: BMC Medical Informatics and Decision Making
Rashmie Abeysinghe, Licong Cui
BACKGROUND: Association Rule Mining (ARM) has been widely used by biomedical researchers to perform exploratory data analysis and uncover potential relationships among variables in biomedical datasets. However, when biomedical datasets are high-dimensional, performing ARM on such datasets will yield a large number of rules, many of which may be uninteresting. Especially for imbalanced datasets, performing ARM directly would result in uninteresting rules that are dominated by certain variables that capture general characteristics...
July 23, 2018: BMC Medical Informatics and Decision Making
Juan Antonio Lossio-Ventura, William Hogan, François Modave, Yi Guo, Zhe He, Xi Yang, Hansi Zhang, Jiang Bian
BACKGROUND: There is strong scientific evidence linking obesity and overweight to the risk of various cancers and to cancer survivorship. Nevertheless, the existing online information about the relationship between obesity and cancer is poorly organized, not evidenced-based, of poor quality, and confusing to health information consumers. A formal knowledge representation such as a Semantic Web knowledge base (KB) can help better organize and deliver quality health information. We previously presented the OC-2-KB (Obesity and Cancer to Knowledge Base), a software pipeline that can automatically build an obesity and cancer KB from scientific literature...
July 23, 2018: BMC Medical Informatics and Decision Making
Rebecca Lin, Muhammad Tuan Amith, Chen Liang, Rui Duan, Yong Chen, Cui Tao
BACKGROUND: Healthcare services, particularly in patient-provider interaction, often involve highly emotional situations, and it is important for physicians to understand and respond to their patients' emotions to best ensure their well-being. METHODS: In order to model the emotion domain, we have created the Visualized Emotion Ontology (VEO) to provide a semantic definition of 25 emotions based on established models, as well as visual representations of emotions utilizing shapes, lines, and colors...
July 23, 2018: BMC Medical Informatics and Decision Making
Rui Zhang, Sisi Ma, Liesa Shanahan, Jessica Munroe, Sarah Horn, Stuart Speedie
BACKGROUND: Cardiac Resynchronization Therapy (CRT) is an established pacing therapy for heart failure patients. The New York Heart Association (NYHA) class is often used as a measure of a patient's response to CRT. Identifying NYHA class for heart failure (HF) patients in an electronic health record (EHR) consistently, over time, can provide better understanding of the progression of heart failure and assessment of CRT response and effectiveness. Though NYHA is rarely stored in EHR structured data, such information is often documented in unstructured clinical notes...
July 23, 2018: BMC Medical Informatics and Decision Making
Haodi Li, Ming Yang, Qingcai Chen, Buzhou Tang, Xiaolong Wang, Jun Yan
BACKGROUND: Extracting relationships between chemicals and diseases from unstructured literature have attracted plenty of attention since the relationships are very useful for a large number of biomedical applications such as drug repositioning and pharmacovigilance. A number of machine learning methods have been proposed for chemical-induced disease (CID) extraction due to some publicly available annotated corpora. Most of them suffer from time-consuming feature engineering except deep learning methods...
July 23, 2018: BMC Medical Informatics and Decision Making
Zhiwei Chen, Zhe He, Xiuwen Liu, Jiang Bian
BACKGROUND: In the past few years, neural word embeddings have been widely used in text mining. However, the vector representations of word embeddings mostly act as a black box in downstream applications using them, thereby limiting their interpretability. Even though word embeddings are able to capture semantic regularities in free text documents, it is not clear how different kinds of semantic relations are represented by word embeddings and how semantically-related terms can be retrieved from word embeddings...
July 23, 2018: BMC Medical Informatics and Decision Making
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