Big-Data on Healthcare Application
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ItemAchieving Data Completeness in Electronic Medical Records: A Conceptual Model and Hypotheses Development( 2018-01-03)This paper aims at proposing a conceptual model of achieving data completeness in electronic medical records (EMR). For this to happen, firstly, we draw on the model of factors influencing data quality management to construct our conceptual model. Secondly, we develop hypotheses of relationships between influencing factors for data completeness and mediators for achieving data completeness in EMR based on the literature. Our conceptual model extends the prior model for factors influencing data quality management by adding a new factor and exploring the relationships between the influencing factors within the context of data completeness in EMR. The proposed conceptual model and the presented hypotheses once empirically validated will be the basis for the development of tools and techniques for achieving data completeness in EMR.
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ItemExploring the potential of big data on the health care delivery value chain (CDVC): a preliminary literature and research agenda( 2018-01-03)Big data analytics (BDA) is emerging as a game changer in healthcare. While the practitioner literature has been speculating on the high potential of BDA in transforming the healthcare sector, few rigorous empirical studies have been conducted by scholars to assess the real potential of BDA. Drawing on the health care delivery value chain (CDVC) and an extensive literature review, this exploratory study aims to discuss current peer-reviewed articles dealing with BDA across the CDVC and discuss future research directions.
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ItemAssociation of the DYX1C1 Gene with Chinese Literacy in a Healthy Chinese Population( 2018-01-03)DYX1C1, the first dyslexia candidate gene, has been associated with developmental dyslexia in different populations, but its influence on reading abilities in the general population is less well known. Copy number variants (CNVs) have been implicated in neurodevelopmental and childhood-onset disorders involving cognitive development in previous studies. In this report, we investigated the extent to which genomic CNVs for the SNP previously linked to dyslexia, -3G/A (rs3743205) in the gene DYX1C1, contribute to Chinese and English literacy in the general population in a Chinese cohort, and whether these processes, in turn, are influenced by environmental factors, such as family income, parents’ education, and IQ. Our findings suggest that the logR ratio (which is a way to detect CNVs) of a previously reported dyslexia-related SNP, -3G/A (rs3743205) is significantly associated with Chinese literacy in a cohort of Chinese children with normal reading abilities.
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ItemLogic Synthesis as an Efficient Means of Minimal Model Discovery from Multivariable Medical Datasets( 2018-01-03)In this paper we review the application of logic synthesis methods for uncovering minimal structures in observational/medical datasets. Traditionally used in digital circuit design, logic synthesis has taken major strides in the past few decades and forms the foundation of some of the most powerful concepts in computer science and data mining. Here we provide a review of current state of research in application of logic synthesis methods for data analysis and provide a demonstrative example for systematic application and reasoning based on these methods.
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ItemDrawing-Based Automatic Dementia Screening Using Gaussian Process Markov Chains( 2018-01-03)Screening tests play an important role for early detection of dementia. Among those widely used screening tests, drawing tests have gained much attention in clinical psychology. Traditional evaluation of drawing tests totally relies on the appearance of drawn picture, but does not consider any time-dependent behaviour. We demonstrated that the processing speed and direction can reflect the decline of cognitive function, and thus may be useful for disease screening. We proposed a model of Gaussian process Markov chains (GPMC) to study the complex associations within the drawing data. Specifically, we modeled the process of drawing in a state-space form, where a drawing state is composed of drawing direction and velocity with consideration of the processing time. For temporal modeling, our scope focused more on discrete-time Markov chains on continuous state space. Because of the short processing time of picture drawing, we applied higher-order of Markov chains to model long-term temporal correlation across drawing states. Gaussian process regression was used for universal function approximation to flexibly infer the state transition function. With Gaussian process prior to the distribution of function space, we could encode high-level function properties such as noisiness, smoothness and periodicity. We also derived an efficient training mechanism for complex Gaussian process regression on bivariate Markov chains. With GPMC, we present an optimal decision rule based on Bayesian decision theory. We applied our proposed method to a drawing test for dementia screening, i.e. interlocking pentagon-drawing test. We tested our models with 256 subjects who are aged from 65 to 95. Finally, comparing to the traditional methods, our models showed remarkable improvement in drawing test for dementia screening.
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ItemBig Data and Parkinson’s Disease: Exploration, Analyses, and Data Challenges.( 2018-01-03)In healthcare, a tremendous amount of clinical and laboratory tests, imaging, prescription and medication data are being collected. Big data analytics on these data aim at early detection of disease which will help in developing preventive measures and in improving patient care. Parkinson disease is the second-most common neurodegenerative disorder in the United States. To find a cure for Parkinson's disease biological, clinical and behavioral data of different cohorts are collected, managed and propagated through Parkinson’s Progression Markers Initiative (PPMI). Applying big data technology to this data will lead to the identification of the potential biomarkers of Parkinson’s disease. Data collected in human clinical studies is imbalanced, heterogeneous, incongruent and sparse. This study focuses on the ways to overcome the challenges offered by PPMI data which is wide and gappy. This work leverages the initial discoveries made through descriptive studies of various attributes. The exploration of data led to identifying the significant attributes. We are further working to build a software suite that enables end to end analysis of Parkinson’s data (from cleaning and curating data, to imputation, to dimensionality reduction, to multivariate correlation and finally to identify potential biomarkers).
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