Sökning: L773:1557 170X OR L773:9781457702204
> (2015-2019) >
Selection of an opt...
Selection of an optimal feature set to predict heart transplantation outcomes
-
- Medved, Dennis (författare)
- Lund University,Lunds universitet,Institutionen för datavetenskap,Institutioner vid LTH,Lunds Tekniska Högskola,Artificiell intelligens och thoraxkirurgisk vetenskap (AICTS),Forskargrupper vid Lunds universitet,Department of Computer Science,Departments at LTH,Faculty of Engineering, LTH,Artificial Intelligence in CardioThoracic Sciences (AICTS),Lund University Research Groups
-
- Nugues, Pierre (författare)
- Lund University,Lunds universitet,Institutionen för datavetenskap,Institutioner vid LTH,Lunds Tekniska Högskola,Artificiell intelligens och thoraxkirurgisk vetenskap (AICTS),Forskargrupper vid Lunds universitet,Department of Computer Science,Departments at LTH,Faculty of Engineering, LTH,Artificial Intelligence in CardioThoracic Sciences (AICTS),Lund University Research Groups
-
- Nilsson, Johan (författare)
- Lund University,Lunds universitet,Thoraxkirurgi,Sektion II,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Hjärt- och lungtransplantation,Forskargrupper vid Lunds universitet,Artificiell intelligens och thoraxkirurgisk vetenskap (AICTS),Thoracic Surgery,Section II,Department of Clinical Sciences, Lund,Faculty of Medicine,Heart and Lung transplantation,Lund University Research Groups,Artificial Intelligence in CardioThoracic Sciences (AICTS)
-
(creator_code:org_t)
- 2016
- 2016
- Engelska 4 s.
-
Ingår i: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. - 1557-170X. - 9781457702204 ; 2016-October, s. 3290-3293
- Relaterad länk:
-
http://dx.doi.org/10...
-
visa fler...
-
https://lup.lub.lu.s...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- Heart transplantation (HT) is a life saving procedure, but a limited donor supply forces the surgeons to prioritize the recipients. The understanding of factors that predict mortality could help the doctors with this task. The objective of this study is to find locally optimal feature sets to predict survival of HT patients for different time periods. To this end, we applied logistic regression together with a greedy forward and backward search. As data source, we used the United Network for Organ Sharing (UNOS) registry, where we extracted adult patients (>17 years) from January 1997 to December 2008. As methods to predict survival, we used the Index for Mortality Prediction After Cardiac Transplantation (IMPACT) and the International Heart Transplant Survival Algorithm (IHTSA). We used the LIBLINEAR library together with the Apache Spark cluster computing framework to carry out the computation and we found feature sets for 1, 5, and 10 year survival for which we obtained area under the ROC curves (AUROC) of 68%, 68%, and 76%, respectively.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)
Hitta via bibliotek
Till lärosätets databas