Analysts predict that over the next two decades, we will witness some of the most significant disruptions to the workforce and work that we have experienced in centuries. Demographic and socioeconomic trends such as rapid urbanization and globalization, combined with even faster advances in technology from mobile internet to increased automation and machine learning, are causing dramatic shifts in the way work gets done in our factories, hospitals, schools and offices. The world of work is changing, and how global leaders respond will influence how well we weather the storm.
The opioid epidemic is growing disproportionately among women. From 1999 through 2016, mortality rates for opioid overdose increased 507% among women, compared to 321% among men, according to the National Institute on Drug Abuse. Deaths related to prescription opioid as well as heroin use increased at nearly twice the rate for women as for men.
“Conducting a survey [to answer a research question] should be the last resort,” said Tom Smith in his keynote presentation at the BigSurv18 Conference in Barcelona, Spain.
While machine learning applications for classifying data items like tweets or news articles have recently experienced tremendous growth, the process of building a labeled training dataset for these methods continues to be a tremendous challenge. A quality labeled training dataset is critical for machine learning. It is the foundation an algorithm uses to learn to classify future data items. Yet human coders must often expend considerable amounts of time and resources to build this dataset, and it can be even more challenging to ensure consistency between human coders.
Too often in the United States, patients are admitted into a health care facility for routine treatment, expecting routine outcomes. They may be cautioned by their healthcare team about the potential for a negative outcome resulting from an infection, but they do not anticipate that it could happen to them.