62 research outputs found
Implementing Telemedicine in Medical Emergency Response: Concept of Operation for a Regional Telemedicine Hub
A regional telemedicine hub, providing linkage of a telemedicine command center with an extended network of clinical experts in the setting of a natural or intentional disaster, may facilitate future disaster response and improve patient outcomes. However, the health benefits derived from the use of telemedicine in disaster response have not been quantitatively analyzed. In this paper, we present a general model of the application of telemedicine to disaster response and evaluate a concept of operations for a regional telemedicine hub, which would create distributed surge capacity using regional telemedicine networks connecting available healthcare and telemedicine infrastructures to external expertise. Specifically, we investigate (1) the scope of potential use of telemedicine in disaster response; (2) the operational characteristics of a regional telemedicine hub using a new discrete-event simulation model of an earthquake scenario; and (3) the benefit that the affected population may gain from a coordinated regional telemedicine network
The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets – improving meta-analysis and prediction of prognosis
BACKGROUND: The number of gene expression studies in the public domain is rapidly increasing, representing a highly valuable resource. However, dataset-specific bias precludes meta-analysis at the raw transcript level, even when the RNA is from comparable sources and has been processed on the same microarray platform using similar protocols. Here, we demonstrate, using Affymetrix data, that much of this bias can be removed, allowing multiple datasets to be legitimately combined for meaningful meta-analyses. RESULTS: A series of validation datasets comparing breast cancer and normal breast cell lines (MCF7 and MCF10A) were generated to examine the variability between datasets generated using different amounts of starting RNA, alternative protocols, different generations of Affymetrix GeneChip or scanning hardware. We demonstrate that systematic, multiplicative biases are introduced at the RNA, hybridization and image-capture stages of a microarray experiment. Simple batch mean-centering was found to significantly reduce the level of inter-experimental variation, allowing raw transcript levels to be compared across datasets with confidence. By accounting for dataset-specific bias, we were able to assemble the largest gene expression dataset of primary breast tumours to-date (1107), from six previously published studies. Using this meta-dataset, we demonstrate that combining greater numbers of datasets or tumours leads to a greater overlap in differentially expressed genes and more accurate prognostic predictions. However, this is highly dependent upon the composition of the datasets and patient characteristics. CONCLUSION: Multiplicative, systematic biases are introduced at many stages of microarray experiments. When these are reconciled, raw data can be directly integrated from different gene expression datasets leading to new biological findings with increased statistical power
Pain as a global public health priority
<p>Abstract</p> <p>Background</p> <p>Pain is an enormous problem globally. Estimates suggest that 20% of adults suffer from pain globally and 10% are newly diagnosed with chronic pain each year. Nevertheless, the problem of pain has primarily been regarded as a medical problem, and has been little addressed by the field of public health.</p> <p>Discussion</p> <p>Despite the ubiquity of pain, whether acute, chronic or intermittent, public health scholars and practitioners have not addressed this issue as a public health problem. The importance of viewing pain through a public health lens allows one to understand pain as a multifaceted, interdisciplinary problem for which many of the causes are the social determinants of health. Addressing pain as a global public health issue will also aid in priority setting and formulating public health policy to address this problem, which, like most other chronic non-communicable diseases, is growing both in absolute numbers and in its inequitable distribution across the globe.</p> <p>Summary</p> <p>The prevalence, incidence, and vast social and health consequences of global pain requires that the public health community give due attention to this issue. Doing so will mean that health care providers and public health professionals will have a more comprehensive understanding of pain and the appropriate public health and social policy responses to this problem.</p
Bayesian Cue Integration as a Developmental Outcome of Reward Mediated Learning
Average human behavior in cue combination tasks is well predicted by Bayesian inference models. As this capability is acquired over developmental timescales, the question arises, how it is learned. Here we investigated whether reward dependent learning, that is well established at the computational, behavioral, and neuronal levels, could contribute to this development. It is shown that a model free reinforcement learning algorithm can indeed learn to do cue integration, i.e. weight uncertain cues according to their respective reliabilities and even do so if reliabilities are changing. We also consider the case of causal inference where multimodal signals can originate from one or multiple separate objects and should not always be integrated. In this case, the learner is shown to develop a behavior that is closest to Bayesian model averaging. We conclude that reward mediated learning could be a driving force for the development of cue integration and causal inference
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