Assessing the Performance of Machine Learning Methods Trained on Public Health Observational Data: A Case Study From COVID‐19 von Pigoli, Davide, Baker, Kieran, Budd, Jobie, Butler, Lorraine, Coppock, Harry, Egglestone, Sabrina, Gilmour, Steven G., Holmes, Chris, Hurley, David, Jersakova, Radka, Kiskin, Ivan, Koutra, Vasiliki, Mellor, Jonathon, Nicholson, George, Packham, Joe, Patel, Selina, Payne, Richard, Roberts, Stephen J., Schuller, Björn W., Tendero‐Cañadas, Ana, Thornley, Tracey, Titcomb, Alexander

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A large-scale and PCR-referenced vocal audio dataset for COVID-19 von Budd, Jobie, Baker, Kieran, Karoune, Emma, Coppock, Harry, Patel, Selina, Payne, Richard, Tendero Cañadas, Ana, Titcomb, Alexander, Hurley, David, Egglestone, Sabrina, Butler, Lorraine, Mellor, Jonathon, Nicholson, George, Kiskin, Ivan, Koutra, Vasiliki, Jersakova, Radka, McKendry, Rachel A., Diggle, Peter, Richardson, Sylvia, Schuller, Björn W., Gilmour, Steven, Pigoli, Davide, Roberts, Stephen, Packham, Josef, Thornley, Tracey, Holmes, Chris

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How normalisation factors influence the interpretations of 3D-printed sensors for electroanalysis von Abdalla, Aya, Perez, Fernando, Cañadas, Ana Tendero, Ray, Santanu, Patel, Bhavik Anil

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Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers von Coppock, Harry, Nicholson, George, Kiskin, Ivan, Koutra, Vasiliki, Baker, Kieran, Budd, Jobie, Payne, Richard, Karoune, Emma, Hurley, David, Titcomb, Alexander, Egglestone, Sabrina, Tendero Cañadas, Ana, Butler, Lorraine, Jersakova, Radka, Mellor, Jonathon, Patel, Selina, Thornley, Tracey, Diggle, Peter, Richardson, Sylvia, Packham, Josef, Schuller, Björn W., Pigoli, Davide, Gilmour, Steven, Roberts, Stephen, Holmes, Chris

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Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers von Coppock, Harry, Nicholson, George, Kiskin, Ivan, Koutra, Vasiliki, Baker, Kieran, Budd, Jobie, Payne, Richard, Karoune, Emma, Hurley, David, Titcomb, Alexander, Egglestone, Sabrina, Cañadas, Ana Tendero, Butler, Lorraine, Jersakova, Radka, Mellor, Jonathon, Patel, Selina, Thornley, Tracey, Diggle, Peter, Richardson, Sylvia, Packham, Josef, Schuller, Björn W, Pigoli, Davide, Gilmour, Steven, Roberts, Stephen, Holmes, Chris

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Statistical Design and Analysis for Robust Machine Learning: A Case Study from COVID-19 von Pigoli, Davide, Baker, Kieran, Budd, Jobie, Butler, Lorraine, Coppock, Harry, Egglestone, Sabrina, Gilmour, Steven G, Holmes, Chris, Hurley, David, Jersakova, Radka, Kiskin, Ivan, Koutra, Vasiliki, Mellor, Jonathon, Nicholson, George, Packham, Joe, Patel, Selina, Payne, Richard, Roberts, Stephen J, Schuller, Björn W, Tendero-Cañadas, Ana, Thornley, Tracey, Titcomb, Alexander

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A large-scale and PCR-referenced vocal audio dataset for COVID-19 von Budd, Jobie, Baker, Kieran, Karoune, Emma, Coppock, Harry, Patel, Selina, Cañadas, Ana Tendero, Titcomb, Alexander, Payne, Richard, Hurley, David, Egglestone, Sabrina, Butler, Lorraine, Mellor, Jonathon, Nicholson, George, Kiskin, Ivan, Koutra, Vasiliki, Jersakova, Radka, McKendry, Rachel A, Diggle, Peter, Richardson, Sylvia, Schuller, Björn W, Gilmour, Steven, Pigoli, Davide, Roberts, Stephen, Packham, Josef, Thornley, Tracey, Holmes, Chris

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The UK COVID-19 Vocal Audio Dataset von Coppock, Harry, Budd, Jobie, Karoune, Emma, Holmes, Chris, Baker, Kieran, Pigoli, Davide, Nicholson, George, Payne, Richard, Kiskin, Ivan, Packham, Josef, Cañadas, Ana Tendero, Patel, Selina, Egglestone, Sabrina, Titcomb, Alexander, Hurley, David, Butler, Lorraine, Thornley, Tracey, Mellor, Jonathon, Roberts, Stephen, Gilmour, Steven, Schuller, Björn, Koutra, Vasiliki, Jersakova, Radka, Diggle, Peter, Richardson, Sylvia

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A large-scale and PCR-referenced vocal audio dataset for COVID-19 von Budd, Jobie, Baker, Kieran, Karoune, Emma, Coppock, Harry, Patel, Selina, Ana Tendero Cañadas, Titcomb, Alexander, Payne, Richard, Hurley, David, Egglestone, Sabrina, Butler, Lorraine, Mellor, Jonathon, Nicholson, George, Kiskin, Ivan, Koutra, Vasiliki, Jersakova, Radka, McKendry, Rachel A, Diggle, Peter, Richardson, Sylvia, Schuller, Björn W, Gilmour, Steven, Pigoli, Davide, Roberts, Stephen, Packham, Josef, Thornley, Tracey, Holmes, Chris

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Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers von Coppock, Harry, Nicholson, George, Kiskin, Ivan, Koutra, Vasiliki, Baker, Kieran, Budd, Jobie, Payne, Richard, Karoune, Emma, Hurley, David, Titcomb, Alexander, Egglestone, Sabrina, Ana Tendero Cañadas, Butler, Lorraine, Jersakova, Radka, Mellor, Jonathon, Patel, Selina, Thornley, Tracey, Diggle, Peter, Richardson, Sylvia, Packham, Josef, Schuller, Björn W, Pigoli, Davide, Gilmour, Steven, Roberts, Stephen, Holmes, Chris

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Statistical Design and Analysis for Robust Machine Learning: A Case Study from COVID-19 von Pigoli, Davide, Baker, Kieran, Budd, Jobie, Butler, Lorraine, Coppock, Harry, Egglestone, Sabrina, Gilmour, Steven G, Holmes, Chris, Hurley, David, Jersakova, Radka, Kiskin, Ivan, Koutra, Vasiliki, Mellor, Jonathon, Nicholson, George, Packham, Joe, Patel, Selina, Payne, Richard, Roberts, Stephen J, Schuller, Björn W, Tendero-Cañadas, Ana, Thornley, Tracey, Titcomb, Alexander

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Factors associated with malaria and arboviral disease in patients with imported febrile syndrome: a retrospective cohort study von López-Amorós, Ana-Isabel, Torrús-Tendero, Diego, Merino de Lucas, Esperanza, Reus Bañuls, Sergio, Boix Martínez, Vicente, Llorens Soriano, Pere, Escribano Cañadas, Isabel, Ramos-Rincón, José-Manuel

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