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BMC Medical Informatics and Decision Making

Georgy Kopanitsa, Ilia Semenov
BACKGROUND: In some healthcare systems, it is common that patients address laboratory test centers directly without a physician's recommendation. This practice is widely spread in Russia with about 28% of patients who visiting laboratory test centers for diagnostics. This causes an issue when patients get no help from the physician in understanding the results. Computer decision support systems proved to efficiently solve a resource consuming task of interpretation of the test results...
July 20, 2018: BMC Medical Informatics and Decision Making
Stephen Mburu, Robert Oboko
BACKGROUND: In low-resource settings, there are numerous socioeconomic challenges such as poverty, inadequate facilities, shortage of skilled health workers, illiteracy and cultural barriers that contribute to high maternal and newborn deaths. To address these challenges, there are several mHealth projects particularly in Sub-Sahara Africa seeking to exploit opportunities provided by over 90% rate of mobile penetration. However, most of these interventions have failed to justify their value proposition to inspire utilization in low-resource settings...
July 17, 2018: BMC Medical Informatics and Decision Making
Kavishwar B Wagholikar, Pralav Dessai, Javier Sanz, Michael E Mendis, Douglas S Bell, Shawn N Murphy
BACKGROUND: Informatics for Integrating Biology and the Bedside (i2b2) is an open source clinical data analytics platform used at over 200 healthcare institutions for querying patient data. The i2b2 platform has several components with numerous dependencies and configuration parameters, which renders the task of installing or upgrading i2b2 a challenging one. Even with the availability of extensive documentation and tutorials, new users often require several weeks to correctly install a functional i2b2 platform...
July 16, 2018: BMC Medical Informatics and Decision Making
Alex J Walker, Seb Bacon, Richard Croker, Ben Goldacre
BACKGROUND: The widely used service provides standard measures which compare prescribing of Clinical Commissioning Groups (CCGs) and English General Practices against that of their peers. Detecting changes in prescribing behaviour compared with peers can help identify missed opportunities for medicines optimisation. Automating the process of detecting these changes is necessary due to the volume of data, but challenging due to variation in prescribing volume for different measures and locations...
July 9, 2018: BMC Medical Informatics and Decision Making
Ignacio Ghersi, Mario Mariño, Mónica Teresita Miralles
BACKGROUND: Recent scientific achievements and technological advances have brought forward a massive display of new or updated medical devices, enabled with highly-developed embedded-control functions and interactivity. From the final decade of the twentieth century, medical beds have particularly been affected by this surge, taking on new forms and functions, while accommodating to established properties that have become well-known for these devices. The past fifteen years have also brought forward changes to conceptual frameworks, concerning the product design and manufacturing processes (standards), as well as the patient (perspectives on patient-care environments and accessibility)...
July 9, 2018: BMC Medical Informatics and Decision Making
Yiqing Zhao, Nooshin J Fesharaki, Hongfang Liu, Jake Luo
BACKGROUND: The use of knowledge models facilitates information retrieval, knowledge base development, and therefore supports new knowledge discovery that ultimately enables decision support applications. Most existing works have employed machine learning techniques to construct a knowledge base. However, they often suffer from low precision in extracting entity and relationships. In this paper, we described a data-driven sublanguage pattern mining method that can be used to create a knowledge model...
July 6, 2018: BMC Medical Informatics and Decision Making
Erik Joukes, Ronald Cornet, Martine C de Bruijne, Nicolette F de Keizer, Ameen Abu-Hanna
BACKGROUND: Healthcare professionals provide care to patients and during that process, record large quantities of data in patient records. Data in an Electronic Health Record should ideally be recorded once and be reusable within the care process as well as for secondary purposes. A common approach to realise this is to let healthcare providers record data in a standardised and structured way at the point of care. Currently, it is not clear to what extent this structured and standardised recording has been adopted by healthcare professionals and what barriers to their adoption exist...
June 28, 2018: BMC Medical Informatics and Decision Making
Ana Respicio, Margarida Moz, Margarida Vaz Pato, Rute Somensi, Cecília Dias Flores
BACKGROUND: Approaches to nurse staffing are commonly concerned with determining the minimum number of care hours according to the illness severity of patients. However, there is a gap in the literature considering multi-skill and multi-shift nurse staffing. This study addresses nurse staffing per skill category, at a strategical decision level, by considering the organization of work in shifts and coping with variability in demand. METHODS: We developed a method to determine the nursing staff levels in a hospital, given the required patient assistance...
June 28, 2018: BMC Medical Informatics and Decision Making
Parvin Tajik, Mohammad Hadi Zafarmand, Aeilko H Zwinderman, Ben W Mol, Patrick M Bossuyt
BACKGROUND: Despite the growing interest in developing markers for predicting treatment response and optimizing treatment decisions, an appropriate methodology to identify, combine and evaluate such markers has been slow to develop. We propose a step-by-step strategy for analysing data from existing randomised trials with the aim of identifying a multi-marker model for guiding decisions about treatment. METHODS: We start with formulating the treatment selection problem, continue with defining the treatment threshold, prepare a list of candidate markers, develop the model, apply the model to estimate individual treatment effects, and evaluate model performance in the study group of patients who meet the trial eligibility criteria...
June 28, 2018: BMC Medical Informatics and Decision Making
Agustin Sancen-Plaza, Raul Santiago-Montero, Humberto Sossa, Francisco J Perez-Pinal, Juan J Martinez-Nolasco, Jose A Padilla-Medina
BACKGROUND: The performance of Computer Aided Diagnosis Systems for early melanoma detection relies mainly on quantitative evaluation of the geometric features corresponding to skin lesions. In these systems, diagnosis is carried out by analyzing four geometric characteristics: asymmetry (A), border (B), color (C) and dimension (D). The main objective of this study is to establish an algorithm for the measurement of asymmetry in biological entities. METHODS: Binary digital images corresponding to lesions are divided into 8 segments from their centroid...
June 27, 2018: BMC Medical Informatics and Decision Making
Joseph Thobias, Achilles Kiwanuka
BACKGROUND: Information and Communication Technologies (ICTs) have been utilised globally for advancing social and economic development. As information becomes key to enlightening development initiatives, the role of mobile technology-based ICT services is becoming more significant. The aim of this study was to design and implement a mHealth data model with an intention of improving mothers' knowledge of Reproductive and Child Health (RCH) services in rural environments and to remind mothers who do not have access to mobile phones to attend antenatal care...
June 25, 2018: BMC Medical Informatics and Decision Making
Richard Jackson, Ismail Kartoglu, Clive Stringer, Genevieve Gorrell, Angus Roberts, Xingyi Song, Honghan Wu, Asha Agrawal, Kenneth Lui, Tudor Groza, Damian Lewsley, Doug Northwood, Amos Folarin, Robert Stewart, Richard Dobson
BACKGROUND: Traditional health information systems are generally devised to support clinical data collection at the point of care. However, as the significance of the modern information economy expands in scope and permeates the healthcare domain, there is an increasing urgency for healthcare organisations to offer information systems that address the expectations of clinicians, researchers and the business intelligence community alike. Amongst other emergent requirements, the principal unmet need might be defined as the 3R principle (right data, right place, right time) to address deficiencies in organisational data flow while retaining the strict information governance policies that apply within the UK National Health Service (NHS)...
June 25, 2018: BMC Medical Informatics and Decision Making
Matthew Shardlow, Riza Batista-Navarro, Paul Thompson, Raheel Nawaz, John McNaught, Sophia Ananiadou
BACKGROUND: Text mining (TM) methods have been used extensively to extract relations and events from the literature. In addition, TM techniques have been used to extract various types or dimensions of interpretative information, known as Meta-Knowledge (MK), from the context of relations and events, e.g. negation, speculation, certainty and knowledge type. However, most existing methods have focussed on the extraction of individual dimensions of MK, without investigating how they can be combined to obtain even richer contextual information...
June 25, 2018: BMC Medical Informatics and Decision Making
Sara Bersche Golas, Takuma Shibahara, Stephen Agboola, Hiroko Otaki, Jumpei Sato, Tatsuya Nakae, Toru Hisamitsu, Go Kojima, Jennifer Felsted, Sujay Kakarmath, Joseph Kvedar, Kamal Jethwani
BACKGROUND: Heart failure is one of the leading causes of hospitalization in the United States. Advances in big data solutions allow for storage, management, and mining of large volumes of structured and semi-structured data, such as complex healthcare data. Applying these advances to complex healthcare data has led to the development of risk prediction models to help identify patients who would benefit most from disease management programs in an effort to reduce readmissions and healthcare cost, but the results of these efforts have been varied...
June 22, 2018: BMC Medical Informatics and Decision Making
Rachel F Eyler, Sara Cordes, Benjamin R Szymanski, Liana Fraenkel
BACKGROUND: As patients become more engaged in decisions regarding their medical care, they must weigh the potential benefits and harms of different treatments. Patients who are low in numeracy may be at a disadvantage when making these decisions, as low numeracy is correlated with less precise representations of numerical magnitude. The current study looks at the feasibility of improving number representations. The aim of this study was to evaluate whether providing a small amount of feedback to adult subjects could improve performance on a number line placement task and to determine characteristics of those individuals who respond best to this feedback...
June 20, 2018: BMC Medical Informatics and Decision Making
Lingling Zhou, Ping Zhao, Dongdong Wu, Cheng Cheng, Hao Huang
BACKGROUND: Hospital crowding is a rising problem, effective predicting and detecting managment can helpful to reduce crowding. Our team has successfully proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in the schistosomiasis and hand, foot, and mouth disease forecasting study. In this paper, our aim is to explore the application of the hybrid ARIMA-NARNN model to track the trends of the new admission inpatients, which provides a methodological basis for reducing crowding...
June 15, 2018: BMC Medical Informatics and Decision Making
Andrea C Tricco, Wasifa Zarin, Erin Lillie, Serena Jeblee, Rachel Warren, Paul A Khan, Reid Robson, Ba' Pham, Graeme Hirst, Sharon E Straus
BACKGROUND: A scoping review to characterize the literature on the use of conversations in social media as a potential source of data for detecting adverse events (AEs) related to health products. METHODS: Our specific research questions were (1) What social media listening platforms exist to detect adverse events related to health products, and what are their capabilities and characteristics? (2) What is the validity and reliability of data from social media for detecting these adverse events? MEDLINE, EMBASE, Cochrane Library, and relevant websites were searched from inception to May 2016...
June 14, 2018: BMC Medical Informatics and Decision Making
Aravindhan Ganesan, Theinmozhi Arulraj, Tahir Choulli, Khaled H Barakat
BACKGROUND: Monoclonal antibodies blocking the Cytotoxic T-lymphocyte antigen 4 (CTLA-4) receptor have revolutionized the field of anti-cancer therapy for the last few years. The human T-cell-based immune responses are modulated by two contradicting signals. CTLA-4 provides a T cell inhibitory signal through its interaction with B7 ligands (B7-1 and B7-2), while CD28 provides a stimulatory signal when interacting with the same ligands. A previous theoretical model has focused on understanding the processes of costimulatory and inhibitory complex formations at the synapse...
June 11, 2018: BMC Medical Informatics and Decision Making
Shamil Haroon, Darren Wooldridge, Jan Hoogewerf, Krishnarajah Nirantharakumar, John Williams, Lina Martino, Neeraj Bhala
BACKGROUND: Alcohol misuse is an important cause of premature disability and death. While clinicians are recommended to ask patients about alcohol use and provide brief interventions and specialist referral, this is poorly implemented in routine practice. We undertook a national consultation to ascertain the appropriateness of proposed standards for recording information about alcohol use in electronic health records (EHRs) in the UK and to identify potential barriers and facilitators to their implementation in practice...
June 7, 2018: BMC Medical Informatics and Decision Making
Amy M Kwon
BACKGROUND: It is a challenge to precisely classify plasma proteomic profiles into their clinical status based solely on their patterns even though distinct patterns of plasma proteomic profiles are regarded as potential to be a biomarker because the profiles have large within-subject variances. METHODS: The present study proposes a rank-based weighted CBR classifier (RWCBR). We hypothesized that a CBR classifier is advantageous when individual patterns are specific and do not follow the general patterns like proteomic profiles, and robust feature weights can enhance the performance of the CBR classifier...
May 31, 2018: BMC Medical Informatics and Decision Making
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