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Health Information Science and Systems

Jessica Pinaire, Jérôme Azé, Sandra Bringay, Paul Landais
BACKGROUND: Patient healthcare trajectory is a recent emergent topic in the literature, encompassing broad concepts. However, the rationale for studying patients' trajectories, and how this trajectory concept is defined remains a public health challenge. Our research was focused on patients' trajectories based on disease management and care, while also considering medico-economic aspects of the associated management. We illustrated this concept with an example: a myocardial infarction (MI) occurring in a patient's hospital trajectory of care...
December 2017: Health Information Science and Systems
Gang Luo
BACKGROUND: Predictive modeling is fundamental to transforming large clinical data sets, or "big clinical data," into actionable knowledge for various healthcare applications. Machine learning is a major predictive modeling approach, but two barriers make its use in healthcare challenging. First, a machine learning tool user must choose an algorithm and assign one or more model parameters called hyper-parameters before model training. The algorithm and hyper-parameter values used typically impact model accuracy by over 40 %, but their selection requires many labor-intensive manual iterations that can be difficult even for computer scientists...
2016: Health Information Science and Systems
Geletaw Sahle
BACKGROUND: Improving maternal health and reducing maternal mortality rate are key concerns. One of the eight millennium development goals adopted at the millennium summit, was to improve maternal health in Ethiopia. This leads towards discovering the factors that hinder postnatal care visit in Ethiopia. METHODS: In this research, knowledge discovery from data (KDD) was applied to identify the factors that hinder postnatal care visits in Ethiopia. Decision tree (using J48 algorithm) and rule induction (using JRip algorithm) techniques were applied on 6558 records of Ethiopian demographic and health survey data...
2016: Health Information Science and Systems
Liang Zhao, Qi Li, Yuanyuan Xue, Jia Jia, Ling Feng
BACKGROUND: In the modern stressful society, growing teenagers experience severe stress from different aspects from school to friends, from self-cognition to inter-personal relationship, which negatively influences their smooth and healthy development. Being timely and accurately aware of teenagers psychological stress and providing effective measures to help immature teenagers to cope with stress are highly valuable to both teenagers and human society. Previous work demonstrates the feasibility to sense teenagers' stress from their tweeting contents and context on the open social media platform-micro-blog...
2016: Health Information Science and Systems
Gang Luo
BACKGROUND: Predictive modeling is a key component of solutions to many healthcare problems. Among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. Most machine learning models give no explanation for their prediction results, whereas interpretability is essential for a predictive model to be adopted in typical healthcare settings. METHODS: This paper presents the first complete method for automatically explaining results for any machine learning predictive model without degrading accuracy...
2016: Health Information Science and Systems
Shinichi Motomura, Muneaki Ohshima, Ning Zhong
[This corrects the article DOI: 10.1186/s13755-015-0012-z.].
2016: Health Information Science and Systems
Wei Liu, Bo Chuen Chung, Rui Wang, Jonathon Ng, Nigel Morlet
Despite the rapid global movement towards electronic health records, clinical letters written in unstructured natural languages are still the preferred form of inter-practitioner communication about patients. These letters, when archived over a long period of time, provide invaluable longitudinal clinical details on individual and populations of patients. In this paper we present three unsupervised approaches, sequential pattern mining (PrefixSpan); frequency linguistic based C-Value; and keyphrase extraction from co-occurrence graphs (TextRank), to automatically extract single and multi-word medical terms without domain-specific knowledge...
2015: Health Information Science and Systems
Shinichi Motomura, Muneaki Ohshima, Ning Zhong
A healthy lifestyle is becoming increasingly important worldwide, and various health monitoring devices that support this trend are currently being developed. Devices measuring blood pressure, weight, temperature, and pulse have been mainstream. In contrast, electroencephalography has been only useful in medical practice and brain research. For an electroencephalograph to be used in health care, it must be small and user-friendly. The conventional electroencephalograph uses more than twelve electrodes attached to a user's head with paste and hence is very precise...
2015: Health Information Science and Systems
Gang Luo
BACKGROUND: Predictive modeling is fundamental for extracting value from large clinical data sets, or "big clinical data," advancing clinical research, and improving healthcare. Machine learning is a powerful approach to predictive modeling. Two factors make machine learning challenging for healthcare researchers. First, before training a machine learning model, the values of one or more model parameters called hyper-parameters must typically be specified. Due to their inexperience with machine learning, it is hard for healthcare researchers to choose an appropriate algorithm and hyper-parameter values...
2015: Health Information Science and Systems
Olugbenga A Adenuga, Ray M Kekwaletswe, Alfred Coleman
Investments in healthcare information and communication technology (ICT) and health information systems (HIS) continue to increase. This is creating immense pressure on healthcare ICT and HIS to deliver and show significance in such investments in technology. It is discovered in this study that integration and interoperability contribute largely to this failure in ICT and HIS investment in healthcare, thus resulting in the need towards healthcare architecture for eHealth. This study proposes an eHealth architectural model that accommodates requirement based on healthcare need, system, implementer, and hardware requirements...
2015: Health Information Science and Systems
Tobias Wartzek, Michael Czaplik, Christoph Hoog Antink, Benjamin Eilebrecht, Rafael Walocha, Steffen Leonhardt
While PhysioNet is a large database for standard clinical vital signs measurements, such a database does not exist for unobtrusively measured signals. This inhibits progress in the vital area of signal processing for unobtrusive medical monitoring as not everybody owns the specific measurement systems to acquire signals. Furthermore, if no common database exists, a comparison between different signal processing approaches is not possible. This gap will be closed by our UnoViS database. It contains different recordings in various scenarios ranging from a clinical study to measurements obtained while driving a car...
2015: Health Information Science and Systems
Manal Almalki, Kathleen Gray, Fernando Martin Sanchez
BACKGROUND: Self-quantification is seen as an emerging paradigm for health care self-management. Self-quantification systems (SQS) can be used for tracking, monitoring, and quantifying health aspects including mental, emotional, physical, and social aspects in order to gain self-knowledge. However, there has been a lack of a systematic approach for conceptualising and mapping the essential activities that are undertaken by individuals who are using SQS in order to improve health outcomes...
2015: Health Information Science and Systems
Guillermo López-Campos, Mónica Aguado-Urda, María Mar Blanco, Alicia Gibello, María Teresa Cutuli, Victoria López-Alonso, Fernando Martín-Sánchez, José F Fernández-Garayzábal
OBJECTIVE: To describe the importance of bioinformatics tools to analyze the big data yielded from new "omics" generation-methods, with the aim of unraveling the biology of the pathogen bacteria Lactococcus garvieae. METHODS: The paper provides the vision of the large volume of data generated from genome sequences, gene expression profiles by microarrays and other experimental methods that require biomedical informatics methods for management and analysis. RESULTS: The use of biomedical informatics methods improves the analysis of big data in order to obtain a comprehensive characterization and understanding of the biology of pathogenic organisms, such as L...
2015: Health Information Science and Systems
Laura I Rusu, Kelly L Wyres, Matthias Reumann, Carlos Queiroz, Alexe Bojovschi, Tom Conway, Saurabh Garg, David J Edwards, Geoff Hogg, Kathryn E Holt
Even with the advent of next-generation sequencing (NGS) technologies which have revolutionised the field of bacterial genomics in recent years, a major barrier still exists to the implementation of NGS for routine microbiological use (in public health and clinical microbiology laboratories). Such routine use would make a big difference to investigations of pathogen transmission and prevention/control of (sometimes lethal) infections. The inherent complexity and high frequency of data analyses on very large sets of bacterial DNA sequence data, the ability to ensure data provenance and automatically track and log all analyses for audit purposes, the need for quick and accurate results, together with an essential user-friendly interface for regular non-technical laboratory staff, are all critical requirements for routine use in a public health setting...
2015: Health Information Science and Systems
Toan D Nguyen, Parnesh Raniga, David G Barnes, Gary F Egan
BACKGROUND: Biomedical imaging research increasingly involves acquiring, managing and processing large amounts of distributed imaging data. Integrated systems that combine data, meta-data and workflows are crucial for realising the opportunities presented by advances in imaging facilities. METHODS: This paper describes the design, implementation and operation of a multi-modality research imaging data management system that manages imaging data obtained from biomedical imaging scanners operated at Monash Biomedical Imaging (MBI), Monash University in Melbourne, Australia...
2015: Health Information Science and Systems
Guido Zuccon, Sankalp Khanna, Anthony Nguyen, Justin Boyle, Matthew Hamlet, Mark Cameron
Early detection of disease outbreaks is critical for disease spread control and management. In this work we investigate the suitability of statistical machine learning approaches to automatically detect Twitter messages (tweets) that are likely to report cases of possible influenza like illnesses (ILI). Empirical results obtained on a large set of tweets originating from the state of Victoria, Australia, in a 3.5 month period show evidence that machine learning classifiers are effective in identifying tweets that mention possible cases of ILI (up to 0...
2015: Health Information Science and Systems
Benjamin Goudey, Mani Abedini, John L Hopper, Michael Inouye, Enes Makalic, Daniel F Schmidt, John Wagner, Zeyu Zhou, Justin Zobel, Matthias Reumann
Genome-wide association studies (GWAS) are a common approach for systematic discovery of single nucleotide polymorphisms (SNPs) which are associated with a given disease. Univariate analysis approaches commonly employed may miss important SNP associations that only appear through multivariate analysis in complex diseases. However, multivariate SNP analysis is currently limited by its inherent computational complexity. In this work, we present a computational framework that harnesses supercomputers. Based on our results, we estimate a three-way interaction analysis on 1...
2015: Health Information Science and Systems
Blanca Gallego, Farah Magrabi, Oscar Perez Concha, Ying Wang, Enrico Coiera
BACKGROUND: The last two decades have seen an unprecedented growth in initiatives aimed to improve patient safety. For the most part, however, evidence of their impact remains controversial. At the same time, the healthcare industry has experienced an also unprecedented growth in the amount and variety of available electronic data. METHODS: In this paper, we provide a review of the use of routinely collected electronic data in the identification, analysis and surveillance of temporal patterns of patient safety...
2015: Health Information Science and Systems
Fernando Martin-Sanchez
No abstract text is available yet for this article.
2015: Health Information Science and Systems
Xiaofei Yang, Jiming Liu, Xiao-Nong Zhou, William Kw Cheung
BACKGROUND: To investigate transmission patterns of an infectious disease, e.g., malaria, it is desirable to use the observed surveillance data to discover the underlying (often hidden) disease transmission networks. Previous studies have provided methods for inferring information diffusion networks in which each node corresponds to an individual person. However, in the case of disease transmission, to effectively propose and implement intervention strategies, it is more realistic and reasonable for policy makers to study the diffusion patterns at a metapopulation level when the disease transmission is affected by mobile population, that is, to consider disease transmission networks in which nodes represent subpopulations, and links indicate their interrelationships...
2014: Health Information Science and Systems
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