{"id":94264,"date":"2020-10-21T16:03:10","date_gmt":"2020-10-21T13:03:10","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/algorithm-accurately-predicted-risks-of-death-from-covid-19-in-england\/"},"modified":"2020-10-21T16:03:10","modified_gmt":"2020-10-21T13:03:10","slug":"algorithm-accurately-predicted-risks-of-death-from-covid-19-in-england","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/algorithm-accurately-predicted-risks-of-death-from-covid-19-in-england\/","title":{"rendered":"#Algorithm accurately predicted risks of death from COVID-19 in England"},"content":{"rendered":"

“#Algorithm accurately predicted risks of death from COVID-19 in England<\/strong>”
\n<\/p>\n

\n An algorithm accurately predicted the risk of someone with COVID-19 becoming seriously ill during the first wave of the pandemic in England, according to new research<\/a>.<\/p>\n

The British government-backed model was developed using anonymized data from more than 8 million adults in 1,205 general<\/a> practices. It then analyzed factors including a person\u2019s age, ethnicity, level of deprivation, body mass index, and existing medical conditions to predict their chance of catching COVID-19 and then dying or being hospitalized.<\/p>\n

The algorithm was tested on two sets of anonymized data, from January to April 2020 and from May 2020 to June 2020, to evaluate whether it could accurately predict\u00a0who would develop a severe illness.\u00a0A study published Tuesday in the British Medical Journal<\/a> concluded that it\u00a0\u201cperformed well\u201d in forecasting risks\u00a0of\u00a0deaths and hospital admissions due to COVID-19.\u00a0\u00a0<\/span><\/p>\n

The researchers found that the 5% of people predicted to be at greatest risk accounted for 75% of the deaths from COVID-19.\u00a0<\/span>The results also support\u00a0existing evidence<\/a> that\u00a0people\u00a0who are male, from ethnic minority backgrounds, and over 70 are at high risk of severe outcomes from infection.<\/p>\n

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Of those who died, 57.4% were male; 16.7% were from ethnic minority backgrounds; 82.5% were aged 70 and over; 32.3% had type 2 diabetes; 29.9% had dementia; and 23.6% were identified as living in a care home.<\/p>\n

The study authors acknowledge that the results should be interpreted with caution as COVID-19 infection rates and prevention measures will change over time. But they add that the model can be updated as the pandemic evolves.<\/span><\/p>\n

Researchers from Queen Mary University of London will now help implement the model in east London general practices \u2014 which cover a population of around 2 million \u2014\u00a0 to assist them in shielding patients, managing current infections, and prioritizing future vaccination programs.<\/p>\n

However, medical experts warned that the algorithm is missing the data and implementation plan it needs to be a\u00a0useful tool for managing the second wave of the pandemic.<\/p>\n

\u201cThe model does not know who actually was exposed to or was infected by the virus that causes COVID-19,\u201d said Derek Hill<\/a>, professor of medical imaging at University College London.<\/p>\n

\u201cThe model has no information about individuals\u2019\u00a0behavior, such as whether they self-isolated, or worked in a high-risk job, washed their hands properly, or wore a mask, nor of whether there was lots of COVID-19 infections in an individual\u2019s neighborhood. These missing data are arguably more important than the data included in the model at predicting who will get serious COVID-19.\u201d<\/p>\n

In its present form, the algorithm app<\/a>ears more useful for understanding the first wave than for managing the second.<\/p>\n

\n Published October 21, 2020 \u2014 13:03 UTC\n <\/p>\n<\/p><\/div>\n