Group items tagged

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia (including 51 non-survivors and 59 discharged patients). The maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator logistic regression model were used for selection of laboratory features. Seven laboratory features selected in the model were: prothrombin activity, urea, white blood cell, interleukin-2 receptor, indirect bilirubin, myoglobin, and fibrinogen degradation products. The signature constructed using the seven features had 98% [93%, 100%] sensitivity and 91% [84%, 99%] specificity in predicting outcome of SARS-CoV-2 pneumonia. Thus it is feasible to establish an accurate prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings.

Rubinstein's basic assertion, which is that economic theory tells us more about economic models than it tells us about economic reality, is a warning: We should listen not only to economists when it comes to predicting the future of work; we should listen also to historians, who often bring a deeper historical perspective to their predictions. Automation will significantly change many people's lives in ways that may be painful and enduring.

"The connection is so strong that, according to the World Bank, a simple measure of inequality predicts about half of the variance in murder rates between American states and between countries around the world. When inequality is high and strips large numbers of men of the usual markers of status - like a good job and the ability to support a family - matters of respect and disrespect loom disproportionately."

The starting point is to note that, for people to be held responsible for their actions, they have to know about certain features of the world. In many cases, even this minimal condition for blameworthiness isn't satisfied. For example, Chow would have struggled to predict that the rise of ridesharing apps would crater the market for taxi medallions in New York City - but so, too, did most of us. By their very nature, technological disruptions are difficult to foresee; if they were easy to predict, early investors in these technologies wouldn't get so rich. Such a low bar for blameworthiness seems too harsh to be plausible; how can any of us be blamed for failing to spot trends that almost no one was able to see, despite the significant material incentives for doing so?

The model, called Deep Claim, predicts both when and how much an insurance company will pay for a given claim in advance of any payment they make. It was trained with three million de-identified claims including parameters like demographic information, diagnoses, treatments, and billed amounts. Using this information, Deep Claim can not only predict the date and amount of payments with reasonable certainty, but also the likeliest reasonings for any claim denial in play.

Land use change-for example, the conversion of natural habitats to agricultural or urban ecosystems-is widely recognized to influence the risk and emergence of zoonotic disease in humans1,2. However, whether such changes in risk are underpinned by predictable ecological changes remains unclear. It has been suggested that habitat disturbance might cause predictable changes in the local diversity and taxonomic composition of potential reservoir hosts, owing to systematic, trait-mediated differences in species resilience to human pressures3,4. Here we analyse 6,801 ecological assemblages and 376 host species worldwide, controlling for research effort, and show that land use has global and systematic effects on local zoonotic host communities. Known wildlife hosts of human-shared pathogens and parasites overall comprise a greater proportion of local species richness (18-72% higher) and total abundance (21-144% higher) in sites under substantial human use (secondary, agricultural and urban ecosystems) compared with nearby undisturbed habitats. The magnitude of this effect varies taxonomically and is strongest for rodent, bat and passerine bird zoonotic host species, which may be one factor that underpins the global importance of these taxa as zoonotic reservoirs. We further show that mammal species that harbour more pathogens overall (either human-shared or non-human-shared) are more likely to occur in human-managed ecosystems, suggesting that these trends may be mediated by ecological or life-history traits that influence both host status and tolerance to human disturbance5,6. Our results suggest that global changes in the mode and the intensity of land use are creating expanding hazardous interfaces between people, livestock and wildlife reservoirs of zoonotic disease.

The differing immune system responses of patients with COVID-19 can help predict who will experience moderate and severe consequences of disease, according to a new study by Yale researchers published July 27 in the journal Nature.

A long-held view of mine-based on solid economic theory and much empirical evidence-is that a global monetary and financial system conducive to a stable global order has three attributes: (1) open capital markets, (2) flexible exchange rates between countries or blocs and (3) a predictable and transparent, or rules-based, monetary policy.