Modeling Big Medical Survival Data Using Decision Tree Analysis with Apache Spark

In many medical studies, an outcome of interest is not only whether an event occurred, but when an event occurred; and an example of this is Alzheimer’s disease (AD). Identifying patients with Mild Cognitive Impairment (MCI) who are likely to develop Alzheimer’s disease (AD) is highly important for AD treatment. Previous studies suggest that not all MCI patients will convert to AD. Massive amounts of data from longitudinal and extensive studies on thousands of Alzheimer’s patients have been generated. Building a computational model that can predict conversion form MCI to AD can be highly beneficial for early intervention and treatment planning for AD. This work presents a big data model that contains machine-learning techniques to determine the level of AD in a participant and predict the time of conversion to AD. The proposed framework considers one of the widely used screening assessment for detecting cognitive impairment called Montreal Cognitive Assessment (MoCA). MoCA data set was collected from different centers and integrated into our large data framework storage using a Hadoop Data File System (HDFS); the data was then analyzed using an Apache Spark framework. The accuracy of the proposed framework was compared with a semi-parametric Cox survival analysis model

Application of Big Data to Support Evidence-Based Public Health Policy Decision-Making for Hearing

Ideally, public health policies are formulated from scientific data; however, policy-specific data are often unavailable. Big data can generate ecologically-valid, high-quality scientific evidence, and therefore has the potential to change how public health policies are formulated. Here, we discuss the use of big data for developing evidence-based hearing health policies, using data collected and analyzed with a research prototype of a data repository known as EVOTION (EVidence-based management of hearing impairments: public health pOlicy-making based on fusing big data analytics and simulaTION), to illustrate our points. Data in the repository consist of audiometric clinical data, prospective real-world data collected from hearing aids and an app, and responses to questionnaires collected for research purposes. To date, we have used the platform and a synthetic dataset to model the estimated risk of noise-induced hearing loss and have shown novel evidence of ways in which external factors influence hearing aid usage patterns. We contend that this research prototype data repository illustrates the value of using big data for policy-making by providing high-quality evidence that could be used to formulate and evaluate the impact of hearing health care policies

Examining links between anxiety, reinvestment and walking when talking by older adults during adaptive gait

Falls by older adults often result in reduced quality of life and debilitating fear of further falls. Stopping walking when talking (SWWT) is a significant predictor of future falls by older adults and is thought to reflect age-related increases in attentional demands of walking. We examine whether SWWT is associated with use of explicit movement cues during locomotion, and evaluate if conscious control (i.e., movement specific reinvestment) is causally linked to falls-related anxiety during a complex walking task. We observed whether twenty-four older adults stopped walking when talking when asked a question during an adaptive gait task. After certain trials, participants completed a visual-spatial recall task regarding walkway features, or answered questions about their movements during the walk. In a subsequent experimental condition, participants completed the walking task under conditions of raised postural threat. Compared to a control group, participants who SWWT reported higher scores for aspects of reinvestment relating to conscious motor processing but not movement self-consciousness. The higher scores for conscious motor processing were preserved when scores representing cognitive function were included as a covariate. There were no group differences in measures of general cognitive function, visual spatial working memory or balance confidence. However, the SWWT group reported higher scores on a test of external awareness when walking, indicating allocation of attention away from task-relevant environmental features. Under conditions of increased threat, participants self-reported significantly greater state anxiety and reinvestment and displayed more accurate responses about their movements during the task. SWWT is not associated solely with age-related cognitive decline or generic increases in age-related attentional demands of walking. SWWT may be caused by competition for phonological resources of working memory associated with consciously processing motor actions and appears to be causally linked with fall-related anxiety and increased vigilance.This research was supported by The Royal Society (IE131576) and British Academy (SG132820)

Design and Rationale of the Cognitive Intervention to Improve Memory in Heart Failure Patients Study

BACKGROUND: Memory loss is an independent predictor of mortality among heart failure patients. Twenty-three percent to 50% of heart failure patients have comorbid memory loss, but few interventions are available to treat the memory loss. The aims of this 3-arm randomized controlled trial were to (1) evaluate efficacy of computerized cognitive training intervention using BrainHQ to improve primary outcomes of memory and serum brain-derived neurotrophic factor levels and secondary outcomes of working memory, instrumental activities of daily living, and health-related quality of life among heart failure patients; (2) evaluate incremental cost-effectiveness of BrainHQ; and (3) examine depressive symptoms and genomic moderators of BrainHQ effect. METHODS: A sample of 264 heart failure patients within 4 equal-sized blocks (normal/low baseline cognitive function and gender) will be randomly assigned to (1) BrainHQ, (2) active control computer-based crossword puzzles, and (3) usual care control groups. BrainHQ is an 8-week, 40-hour program individualized to each patient's performance. Data collection will be completed at baseline and at 10 weeks and 4 and 8 months. Descriptive statistics, mixed model analyses, and cost-utility analysis using intent-to-treat approach will be computed. CONCLUSIONS: This research will provide new knowledge about the efficacy of BrainHQ to improve memory and increase serum brain-derived neurotrophic factor levels in heart failure. If efficacious, the intervention will provide a new therapeutic approach that is easy to disseminate to treat a serious comorbid condition of heart failure

Cognitive and mood assessment tools for use in stroke

No abstract available

Integrating personal media and digital TV with QoS guarantees using virtualized set-top boxes: architecture and performance measurements

Nowadays, users consume a lot of functionality in their home coming from a service provider located in the Internet. While the home network is typically shielded off as much as possible from the `outside world', the supplied services could be greatly extended if it was possible to use local information. In this article, an extended service is presented that integrates the user's multimedia content, scattered over multiple devices in the home network, into the Electronic Program Guide (EPG) of the Digital TV. We propose to virtualize the set-top box, by migrating all functionality except user interfacing to the service provider infrastructure. The media in the home network is discovered through standard Universal Plug and Play (UPnP), of which the QoS functionality is exploited to ensure high quality playback over the home network, that basically is out of the control of the service provider. The performance of the subsystems are analysed

Natural history of falls in an incident cohort of Parkinson’s disease: early evolution, risk and protective features

The natural history of falls in early Parkinson’s disease (PD) is poorly understood despite the profound effect of falls on outcome. The primary aim of this study was to describe the natural history of falls, and characterise fallers over 54 months in 99 newly diagnosed people with PD. Seventy-nine (79.7%) participants fell over 54 months and 20 (20.3%) remained falls-naïve. Twenty six (26.2%) reported retrospective falls at baseline. Gait outcomes, disease severity and self-efficacy significantly discriminated across groups. Subjective cognitive complaints emerged as the only significant cognitive predictor. Without exception, outcomes were better for non-fallers compared with fallers at any time point. Between group differences for 54 month fallers and non-fallers were influenced by the inclusion of retrospective fallers and showed a broader range of discriminant characteristics, notably stance time variability and balance self-efficacy. Single fallers (n = 7) were significantly younger than recurrent fallers (n = 58) by almost 15 years (P = 0.013). Baseline performance in early PD discriminates fallers over 54 months, thereby identifying those at risk of falls. Clinical profiles for established and emergent fallers are to some extent distinct. These results reiterate the need for timely interventions to improve postural control and gait

Systems modeling of white matter microstructural abnormalities in Alzheimer's disease

INTRODUCTION: Microstructural abnormalities in white matter (WM) are often reported in Alzheimer's disease (AD). However, it is unclear which brain regions have the strongest WM changes in presymptomatic AD and what biological processes underlie WM abnormality during disease progression. METHODS: We developed a systems biology framework to integrate matched diffusion tensor imaging (DTI), genetic and transcriptomic data to investigate regional vulnerability to AD and identify genetic risk factors and gene subnetworks underlying WM abnormality in AD. RESULTS: We quantified regional WM abnormality and identified most vulnerable brain regions. A SNP rs2203712 in CELF1 was most significantly associated with several DTI-derived features in the hippocampus, the top ranked brain region. An immune response gene subnetwork in the blood was most correlated with DTI features across all the brain regions. DISCUSSION: Incorporation of image analysis with gene network analysis enhances our understanding of disease progression and facilitates identification of novel therapeutic strategies for AD