| Autor: |
Doudesis, Dimitrios, Lee, Kuan Ken, Yang, Jason, Wereski, Ryan, Shah, Anoop S V, Tsanas, Athanasios, Anand, Atul, Pickering, John W, Than, Martin P, Mills, Nicholas L, Mills, Nicholas L, Strachan, Fiona E, Tuck, Christopher, Shah, Anoop SV, Anand, Atul, Chapman, Andrew R, Ferry, Amy V, Lee, Kuan Ken, Doudesis, Dimitrios, Bularga, Anda, Wereski, Ryan, Taggart, Caelan, Lowry, Matthew TH, Mendusic, Filip, Kimenai, Dorien M, Sandeman, Dennis, Adamson, Philip D, Stables, Catherine L, Vallejos, Catalina A, Tsanas, Athanasios, Marshall, Lucy, Stewart, Stacey D, Fujisawa, Takeshi, Hautvast, Mischa, McPherson, Jean, McKinlay, Lynn, Ford, Ian, Newby, David E, Fox, Keith AA, Berry, Colin, Walker, Simon, Weir, Christopher J, Gray, Alasdair, Collinson, Paul O, Apple, Fred S, Reid, Alan, Cruikshank, Anne, Findlay, Iain, Amoils, Shannon, McAllister, David A, Maguire, Donogh, Stevens, Jennifer, Norrie, John, Andrews, Jack PM, Moss, Alastair, Anwar, Mohamed S, Hung, John, Malo, Jonathan, Fischbacher, Colin, Croal, Bernard L, Leslie, Stephen J, Keerie, Catriona, Parker, Richard A, Walker, Allan, Harkess, Ronnie, Wackett, Tony, Armstrong, Roma, Stirling, Laura, MacDonald, Claire, Sadat, Imran, Finlay, Frank, Charles, Heather, Linksted, Pamela, Young, Stephen, Alexander, Bill, Duncan, Chris |
| Zdroj: |
The Lancet Digital Health; May 2022, Vol. 4 Issue: 5 pe300-e308, 9p |
| Abstrakt: |
Diagnostic pathways for myocardial infarction rely on fixed troponin thresholds, which do not recognise that troponin varies by age, sex, and time within individuals. To overcome this limitation, we recently introduced a machine learning algorithm that predicts the likelihood of myocardial infarction. Our aim was to evaluate whether this algorithm performs well in routine clinical practice and predicts subsequent events. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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