A machine learning based exploration of COVID-19 mortality risk

Early prediction of patient mortality risks during a pandemic can decrease mortality by assuring efficient resource allocation and treatment planning. This study aimed to develop and compare prognosis prediction machine learning models based on invasive laboratory and noninvasive clinical and demographic data from patients’ day of admission. Three Support Vector Machine (SVM) models were developed and compared using invasive, noninvasive, and both groups. The results suggested that non-invasive features could provide mortality predictions that are similar to the invasive and roughly on par with the joint model. Feature inspection results from SVM-RFE and sparsity analysis displayed that, compared with the invasive model, the non-invasive model can provide better performances with a fewer number of features, pointing to the presence of high predictive information contents in several non-invasive features, including SPO2, age, and cardiovascular disorders. Furthermore, while the invasive model was able to provide better mortality predictions for the imminent future, non-invasive features displayed better performance for more distant expiration intervals. Early mortality prediction using non-invasive models can give us insights as to where and with whom to intervene. Combined with novel technologies, such as wireless wearable devices, these models can create powerful frameworks for various medical assignments and patient triage

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations

Neural data for Zabeh et al. 2023

Provide data for the following publication: Zabeh et al. 2023, Traveling waves in the monkey frontoparietal network predict recent reward memory</p

How can we detect and analyze traveling waves in human brain oscillations?

The brain is a complex, interconnected network and the large-scale spatiotemporal coordination of neuronal activity is vital for cognition and behavior. Prior studies have proposed that traveling waves of brain oscillations are one mechanism that helps coordinate complex neuronal processes and are crucial for cognition. Traveling waves consist of oscillations that propagate progressively across the cortex and previous studies have shown that these waves play a foundational role for learning, memory processing, and memory consolidation and a range of other behaviors across multiple species. The prevalence of traveling waves in cognition thus indicates that spatiotemporal patterns of neuronal oscillations may coordinate multiple neuronal brain networks and impact behavior. Even though there are several different approaches for analyzing traveling waves using electrophysiological recordings, computational tools targeting the analysis and visualization and understanding of traveling waves are still rare. We briefly review the literature on human intracranial electroencephalography (iEEG), which has shown that traveling waves play an important role in cognition. We then describe a statistical methodology based on circular–linear regression for the detection and analysis of traveling waves from human electrophysiological oscillations. We hope that this approach will provide a more mechanistic understanding of the coordination of neurons across space and time

Traveling waves in frontal and parietal areas encode recent reward history

This OSF page hosts data from the publication “Traveling waves in frontal and parietal areas encode recent reward history" by Zabeh, Foley, Jacobs, and Gottleib