Additional file 1: of The UAE healthy future study: a pilot for a prospective cohort study of 20,000 United Arab Emirates nationals

Autor: Abdishakur Abdulle, Alnaeemi, Abdullah, Aljunaibi, Abdullah, Abdulrahman Al Ali, Saedi, Khaled Al, Eiman Al Zaabi, Oumeziane, Naima, Bastaki, Marina Al, Al-Houqani, Mohammed, Maskari, Fatma Al, Dhaheri, Ayesha Al, Shah, Syed M., Loney, Tom, El-Sadig, Mohamed, Abderrahim Oulhaj, Wareth, Leila, Mahmeed, Wael Al, Alsafar, Habiba, Hirsch, Benjamin, Anouti, Fatme Al, Yaaqoub, Jamila, Inman, Claire, Hamiz, Aisha Al, Hosani, Ayesha Al, Haji, Muna, Teeb Alsharid, Thekra Al Zaabi, Maisary, Fatima Al, Galani, Divya, Sprosen, Tim, Shahawy, Omar El, Jiyoung Ahn, Kirchhoff, Tomas, Ramasamy, Ravichandran, Schmidt, Ann, Hayes, Richard, Sherman, Scott, Ali, Raghib

Participant Completed Questionnaire. (DOCX 82Â kb)

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a32ead44cdf49071d0d1b464dde77216

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Defining disease phenotypes in primary care electronic health records by a machine learning approach:A case study in identifying rheumatoid arthritis

Autor: Zhou, Shang Ming, Fernandez-Gutierrez, Fabiola, Kennedy, Jonathan, Cooksey, Roxanne, Atkinson, Mark, Denaxas, Spiros, Siebert, Stefan, Dixon, William G., O'Neill, Terence W., Choy, Ernest, Sudlow, Cathie, Brophy, Sinead, Danesh, John, Allen, Naomi, Blaveri, Ekaterini, Brannan, Rachael, Brayne, Carol, Chaturvedi, Nish, Collins, Rory, DeLusignan, Simon, Desai, Parul, Eastwood, Sophie, Gallacher, John, Hemingway, Harry, Hotopf, Matthew, Landray, Martin, Lyons, Ronan, McGilchrist, Mark, Moller, Henrik, Pringle, Mike, Sprosen, Tim, Strachan, David, Sullivan, Frank, Woodfield, Rebecca, Zhang, Qiuli, Flaig, Robin

Publikováno v: Zhou, S M, Fernandez-Gutierrez, F, Kennedy, J, Cooksey, R, Atkinson, M, Denaxas, S, Siebert, S, Dixon, W G, O'Neill, T W, Choy, E, Sudlow, C, Brophy, S, Danesh, J, Allen, N, Blaveri, E, Brannan, R, Brayne, C, Chaturvedi, N, Collins, R, DeLusignan, S, Desai, P, Eastwood, S, Gallacher, J, Hemingway, H, Hotopf, M, Landray, M, Lyons, R, McGilchrist, M, Moller, H, Pringle, M, Sprosen, T, Strachan, D, Sullivan, F, Woodfield, R, Zhang, Q & Flaig, R 2016, ' Defining disease phenotypes in primary care electronic health records by a machine learning approach : A case study in identifying rheumatoid arthritis ', PL o S One, vol. 11, no. 5, e0154515 . https://doi.org/10.1371/journal.pone.0154515

Objectives: 1) To use data-driven method to examine clinical codes (risk factors) of a medical condition in primary care electronic health records (EHRs) that can accurately predict a diagnosis of the condition in secondary care EHRs. 2) To develop a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______2761::76f74f96f694e5cad437207c303bf783
https://kclpure.kcl.ac.uk/en/publications/0e83096f-4201-4e4a-ac1b-4f374dbedc64

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