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Machine learning heeft grote voorspelkracht. Kan die worden ingezet om gezondheidsrisico’s van te voren in te schatten? Kan machine learning helpen bij een preventief gezondheidsbeleid? CPB’er Emile Cammeraat bespreekt het in zijn column. →

Setbacks such as dismissal or illness can turn the lives of people upside down. This study shows that such adverse events can be anticipated in advance and that their occurrence is strongly interrelated. →

Setbacks such as dismissal or illness can turn the lives of people upside down. This study shows that such adverse events can be anticipated in advance and that their occurrence is strongly interrelated. →

Setbacks such as dismissal or illness can turn the lives of people upside down. This study shows that such adverse events can be anticipated in advance and that their occurrence is strongly interrelated. →

Setbacks such as dismissal or illness can turn the lives of people upside down. This study shows that such adverse events can be anticipated in advance and that their occurrence is strongly interrelated. →

Setbacks such as dismissal or illness can turn the lives of people upside down. This study shows that such adverse events can be anticipated in advance and that their occurrence is strongly interrelated. →

Nederlandse financiële stelsel kan schokken opvangen in tijden van economische onzekerheid. →

Nederlandse financiële stelsel kan schokken opvangen in tijden van economische onzekerheid. →

Nederlandse financiële stelsel kan schokken opvangen in tijden van economische onzekerheid. →

Het Nederlandse financiële stelsel heeft de coronacrisis goed doorstaan en kan toekomstige schokken opvangen. Er zijn wel risico’s die elkaar kunnen versterken, aangedreven door hoge energieprijzen en inflatie, en daardoor een grotere impact kunnen hebben. →