Benjamin Wild

Jan 9, 2025 | nature.com | Benjamin Wild |Maik Pietzner |Harry Hemingway |Claudia Langenberg

AbstractThe COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1741 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,489 UK Biobank participants.

Nov 1, 2024 | tolerance.ca | Benjamin Wild |Manchester Metropolitan

By Benjamin Wild, Reader in Fashion Narratives, Manchester Metropolitan University The fashion industry is mobilising its marketing machine to encourage Americans to vote in November’s presidential election, and it’s clear they want Kamala Harris to win. The industry’s intervention in the race to the White House has increased since 2016, when US Vogue endorsed Hilary Clinton.

Nov 1, 2024 | theconversation.com | Benjamin Wild

The fashion industry is mobilising its marketing machine to encourage Americans to vote in November’s presidential election, and it’s clear they want Kamala Harris to win. The industry’s intervention in the race to the White House has increased since 2016, when US Vogue endorsed Hilary Clinton. This was the first time in its 100-year history that the fashion bible had put its weight behind a presidential candidate.

Jun 7, 2024 | progresso.com.br | Benjamin Wild |Manchester Metropolitan

Modelo segurando bolsa feita com material reciclado da gripe Stella McCartney - Crédito: Reprodução Instagram De acordo com relatórios recentes da indústria da moda , a narrativa positiva está se tornando mais proeminente à medida que as marcas procuram demonstrar a sua responsabilidade social, estabelecendo relações mais profundas com os consumidores.

May 19, 2024 | nature.com | Benjamin Wild |Maik Pietzner |Stefan Hegselmann |Harry Hemingway |Claudia Langenberg |Ulf Landmesser | +1 more

AbstractThe COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1883 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,460 UK Biobank.