Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values

This work is motivated by the needs of predictive analytics on healthcare data as represented by Electronic Medical Records. Such data is invariably problematic: noisy, with missing entries, with imbalance in classes of interests, leading to serious bias in predictive modeling. Since standard data mining methods often produce poor performance measures, we argue for development of specialized techniques of data-preprocessing and classification. In this paper, we propose a new method to simultaneously classify large datasets and reduce the effects of missing values. It is based on a multilevel framework of the cost-sensitive SVM and the expected maximization imputation method for missing values, which relies on iterated regression analyses. We compare classification results of multilevel SVM-based algorithms on public benchmark datasets with imbalanced classes and missing values as well as real data in health applications, and show that our multilevel SVM-based method produces fast, and more accurate and robust classification results.Comment: arXiv admin note: substantial text overlap with arXiv:1503.0625

Prediction in HRM research–A gap between rhetoric and reality

There are broadly two dimensions on which researchers can evaluate their statistical models: explanatory power and predictive power. Using data on job satisfaction in ageing workforces, we empirically highlight the importance of distinguishing between these two dimensions clearly by showing that a model with a certain degree of explanatory power can produce vastly different levels of predictive power and vice versa—in the same and different contexts. In a further step, we review all the papers published in three top-tier human resource management journals between 2014 and 2018 to show that researchers generally confuse explanation and prediction. Specifically, while almost all authors rely solely on explanatory power assessments (i.e., assessing whether the coefficients are significant and in the hypothesised direction), they also derive practical recommendations, which inherently result from a predictive scenario. Based on our results, we provide HRM researchers recommendations on how to improve the rigour of their explanatory studies

Is Middle-Upper Arm Circumference "normally" distributed? Secondary data analysis of 852 nutrition surveys.

BACKGROUND: Wasting is a major public health issue throughout the developing world. Out of the 6.9 million estimated deaths among children under five annually, over 800,000 deaths (11.6 %) are attributed to wasting. Wasting is quantified as low Weight-For-Height (WFH) and/or low Mid-Upper Arm Circumference (MUAC) (since 2005). Many statistical procedures are based on the assumption that the data used are normally distributed. Analyses have been conducted on the distribution of WFH but there are no equivalent studies on the distribution of MUAC. METHODS: This secondary data analysis assesses the normality of the MUAC distributions of 852 nutrition cross-sectional survey datasets of children from 6 to 59 months old and examines different approaches to normalise "non-normal" distributions. RESULTS: The distribution of MUAC showed no departure from a normal distribution in 319 (37.7 %) distributions using the Shapiro-Wilk test. Out of the 533 surveys showing departure from a normal distribution, 183 (34.3 %) were skewed (D'Agostino test) and 196 (36.8 %) had a kurtosis different to the one observed in the normal distribution (Anscombe-Glynn test). Testing for normality can be sensitive to data quality, design effect and sample size. Out of the 533 surveys showing departure from a normal distribution, 294 (55.2 %) showed high digit preference, 164 (30.8 %) had a large design effect, and 204 (38.3 %) a large sample size. Spline and LOESS smoothing techniques were explored and both techniques work well. After Spline smoothing, 56.7 % of the MUAC distributions showing departure from normality were "normalised" and 59.7 % after LOESS. Box-Cox power transformation had similar results on distributions showing departure from normality with 57 % of distributions approximating "normal" after transformation. Applying Box-Cox transformation after Spline or Loess smoothing techniques increased that proportion to 82.4 and 82.7 % respectively. CONCLUSION: This suggests that statistical approaches relying on the normal distribution assumption can be successfully applied to MUAC. In light of this promising finding, further research is ongoing to evaluate the performance of a normal distribution based approach to estimating the prevalence of wasting using MUAC

A novel, efficient method for estimating the prevalence of acute malnutrition in resource-constrained and crisis-affected settings: A simulation study.

INTRODUCTION: The assessment of the prevalence of acute malnutrition in children under five is widely used for the detection of emergencies, planning interventions, advocacy, and monitoring and evaluation. This study examined PROBIT Methods which convert parameters (mean and standard deviation (SD)) of a normally distributed variable to a cumulative probability below any cut-off to estimate acute malnutrition in children under five using Middle-Upper Arm Circumference (MUAC). METHODS: We assessed the performance of: PROBIT Method I, with mean MUAC from the survey sample and MUAC SD from a database of previous surveys; and PROBIT Method II, with mean and SD of MUAC observed in the survey sample. Specifically, we generated sub-samples from 852 survey datasets, simulating 100 surveys for eight sample sizes. Overall the methods were tested on 681 600 simulated surveys. RESULTS: PROBIT methods relying on sample sizes as small as 50 had better performance than the classic method for estimating and classifying the prevalence of acute malnutrition. They had better precision in the estimation of acute malnutrition for all sample sizes and better coverage for smaller sample sizes, while having relatively little bias. They classified situations accurately for a threshold of 5% acute malnutrition. Both PROBIT methods had similar outcomes. CONCLUSIONS: PROBIT Methods have a clear advantage in the assessment of acute malnutrition prevalence based on MUAC, compared to the classic method. Their use would require much lower sample sizes, thus enable great time and resource savings and permit timely and/or locally relevant prevalence estimates of acute malnutrition for a swift and well-targeted response

Functional structure of the mitral cell dendritic tuft in the rat olfactory bulb

Author Posting. © Society for Neuroscience, 2008. This article is posted here by permission of Society for Neuroscience for personal use, not for redistribution. The definitive version was published in Journal of Neuroscience 28 (2008): 4057-4068, doi:10.1523/JNEUROSCI.5296-07.2008.The input–output transform performed by mitral cells, the principal projection neurons of the olfactory bulb, is one of the key factors in understanding olfaction. We used combined calcium and voltage imaging from the same neuron and computer modeling to investigate signal processing in the mitral cells, focusing on the glomerular dendritic tuft. The main finding was that the dendritic tuft functions as a single electrical compartment for subthreshold signals within the range of amplitudes detectable by voltage-sensitive dye recording. These evoked EPSPs had uniform characteristics throughout the glomerular tuft. The Ca2+ transients associated with spatially uniform subthreshold synaptic potentials were comparable but not equal in amplitude in all regions. The average range of normalized amplitudes of the EPSP-driven Ca2+ signals from different locations on dendritic branches in the glomerular tuft was relatively narrow and appeared to be independent of the dendritic surface-to-volume ratio. The computer simulations constrained by the imaging data indicated that a synchronized activation of ~100 synapses randomly distributed on tuft branches was sufficient to generate spatially homogenous EPSPs. This number of activated synapses is consistent with the data from anatomical studies. Furthermore, voltage attenuation of the EPSP along the primary dendrite at physiological temperature was weak compared with other cell types. In the model, weak attenuation of the EPSP along the primary dendrite could be accounted for by passive electrical properties of the mitral cell.This work was supported by National Institutes of Health Grant NS4273

A robust method for the amplification of RNA in the sense orientation

BACKGROUND: Small quantities of RNA (1–4 μg total RNA) available from biological samples frequently require a single round of amplification prior to analysis, but current amplification strategies have limitations that may restrict their usefulness in downstream genomic applications. The Eberwine amplification method has been extensively validated but is limited by its ability to produce only antisense RNA. Alternatives lack extensive validation and are often confounded by problems with bias or yield attributable to their greater biological and technical complexity. RESULTS: To overcome these limitations, we have developed a straightforward and robust protocol for amplification of RNA in the sense orientation. This protocol is based upon Eberwine's method but incorporates elements of more recent amplification techniques while avoiding their complexities. Our technique yields greater than 100-fold amplification, generates long transcript, and produces mRNA that is well suited for use with microarray applications. Microarrays performed with RNA amplified using this protocol demonstrate minimal amplification bias and high reproducibility. CONCLUSION: The protocol we describe here is readily adaptable for the production of sense or antisense, labeled or unlabeled RNA from intact or partially-degraded prokaryotic or eukaryotic total RNA. The method outperforms several commercial RNA amplification kits and can be used in conjunction with a variety of microarray platforms, such as cDNA arrays, oligonucleotide arrays, and Affymetrix GeneChip™ arrays

Getting back to the event: COVID-19, attendance and perceived importance of protective measures

Current COVID-19 realities impose additional challenges to sporting event organisers who now have to consider and include new protective measures for the safety and security of both active and passive participants. This study focuses on event consumers and issues related to their intention to attend future sporting events and their perception of how important they find some of the safety-related protective measures when attending sporting events following the COVID-19 crisis. Based on the empirical study of residents from one Middle East and two European countries, the results suggest that, once all restrictions on movement and sporting event attendance is allowed to resume, most of the respondents will attend events in their home country within few weeks. In addition, the respondents from a country that experienced more severe consequences of the pandemic perceive all protective measures as more important than respondents from countries that were less affected

Asymptomatic Severe and Moderate Aortic Stenosis:Time for Appraisal of Treatment Indications

Over the last decades, we have witnessed considerable improvements in diagnostics and risk stratification of patients with significant aortic stenosis (AS), paralleled by advances in operative and anesthetic techniques. In addition, accumulating evidence points to the potential benefit of early valve replacement in such patients prior to the onset of symptoms. In parallel, interventional randomized trials have proven the benefit of transcatheter aortic valve replacement in comparison to a surgical approach to valve replacement over a broad risk spectrum in symptomatic patients with AS. This article reviews contemporary management approaches and scrutinizes open questions regarding timing and mode of intervention in asymptomatic patients with severe AS. We also discuss the challenges surrounding the management of symptomatic patients with moderate AS as well as emerging dilemmas related to the concept of a life-long treatment strategy for patients with AS.</p

Simulating Majorana zero modes on a noisy quantum processor

The simulation of systems of interacting fermions is one of the most anticipated applications of quantum computers. The most interesting simulations will require a fault-tolerant quantum computer, and building such a device remains a long-term goal. However, the capabilities of existing noisy quantum processors have steadily improved, sparking an interest in running simulations that, while not necessarily classically intractable, may serve as device benchmarks and help elucidate the challenges to achieving practical applications on near-term devices. Systems of non-interacting fermions are ideally suited to serve these purposes. While they display rich physics and generate highly entangled states when simulated on a quantum processor, their classical tractability enables experimental results to be verified even at large system sizes that would typically defy classical simulation. In this work, we use a noisy superconducting quantum processor to prepare Majorana zero modes as eigenstates of the Kitaev chain Hamiltonian, a model of non-interacting fermions. Our work builds on previous experiments with non-interacting fermionic systems. Previous work demonstrated error mitigation techniques applicable to the special case of Slater determinants. Here, we show how to extend these techniques to the case of general fermionic Gaussian states, and demonstrate them by preparing Majorana zero modes on systems of up to 7 qubits.Comment: 12 pages, 6 figure