Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

Autor: Ivan Protsyuk, Martin G. Seneviratne, Andre Saraiva, Natalie Harris, Hugh Montgomery, Mustafa Suleyman, Xavier Glorot, Dominic King, Jack W. Rae, Clifton R. Baker, Alistair Connell, Suman V. Ravuri, Trevor Back, Clemens Meyer, Nenad Tomasev, Harry Askham, Michal Zielinski, Ruth M. Reeves, Joseph R. Ledsam, Shakir Mohamed, Thomas F. Osborne, Cian Hughes, Chris Laing, Alan Karthikesalingam, Valerio Magliulo, Anne Mottram, Christopher Nielson, Sebastien Baur, Julien Cornebise, Demis Hassabis, Geraint Rees

Publikováno v: Nature Protocols. 16:2765-2787

Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electron

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1547705a8f9b73ddd1995e08dd7f633d
https://doi.org/10.1038/s41596-021-00513-5

A clinically applicable approach to continuous prediction of future acute kidney injury

Autor: Hugh Montgomery, Alan Karthikesalingam, Xavier Glorot, Christopher Nielson, Harry Askham, Suman V. Ravuri, Trevor Back, Joseph R. Ledsam, Michal Zielinski, Kelly S. Peterson, Geraint Rees, Alistair Connell, Nenad Tomasev, Julien Cornebise, Ivan Protsyuk, Andre Saraiva, Demis Hassabis, Cian Hughes, Chris Laing, Ruth M. Reeves, Shakir Mohamed, Dominic King, Anne Mottram, Jack W. Rae, Mustafa Suleyman, Clemens Meyer, Clifton R. Baker

Publikováno v: Nature

The early prediction of deterioration could have an important role in supporting healthcare professionals, as an estimated 11% of deaths in hospital follow a failure to promptly recognize and treat deteriorating patients1. To achieve this goal requir

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c3716eea45fa33fb37c38efb1a54cdd0
https://doi.org/10.1038/s41586-019-1390-1

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

Autor: Nenad, Tomašev, Natalie, Harris, Sebastien, Baur, Anne, Mottram, Xavier, Glorot, Jack W, Rae, Michal, Zielinski, Harry, Askham, Andre, Saraiva, Valerio, Magliulo, Clemens, Meyer, Suman, Ravuri, Ivan, Protsyuk, Alistair, Connell, Cían O, Hughes, Alan, Karthikesalingam, Julien, Cornebise, Hugh, Montgomery, Geraint, Rees, Chris, Laing, Clifton R, Baker, Thomas F, Osborne, Ruth, Reeves, Demis, Hassabis, Dominic, King, Mustafa, Suleyman, Trevor, Back, Christopher, Nielson, Martin G, Seneviratne, Joseph R, Ledsam, Shakir, Mohamed

Publikováno v: Nature protocols. 16(6)

Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electron

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid________::b6eee043792243fcb4afed951517d636
https://pubmed.ncbi.nlm.nih.gov/33953393

Acute Kidney Injury Risk Prediction in Patients Undergoing Coronary Angiography in a National Veterans Health Administration Cohort With External Validation

Autor: Edward D. Siew, Thomas M. Maddox, Thomas T. Tsai, Todd A. MacKenzie, John S. Rumsfeld, Jeremiah R. Brown, Mary E. Plomondon, Christopher Nielson, Clifton R. Baker, Michael E. Matheny, Frederic S. Resnic, James Fly

Publikováno v: Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, Vol 4, Iss 12, Pp n/a-n/a (2015)
Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease

Background Acute kidney injury ( AKI ) occurs frequently after cardiac catheterization and percutaneous coronary intervention. Although a clinical risk model exists for percutaneous coronary intervention, no models exist for both procedures, nor do e

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::81378d77c312420fb7922a1e5567f205
https://doaj.org/article/8a046d5a5be04eebab43bbd8d527ed88