Brain death prediction based on ensembled artificial neural networks in neurosurgical intensive care unit
✍ Scribed by Quan Liu; Xingran Cui; Maysam F. Abbod; Sheng-Jean Huang; Yin-Yi Han; Jiann-Shing Shieh
- Publisher
- Elsevier
- Year
- 2011
- Tongue
- English
- Weight
- 657 KB
- Volume
- 42
- Category
- Article
- ISSN
- 1876-1070
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✦ Synopsis
The demand for organs transplantation has becoming a major issue worldwide. Transplantation of organs from brain-dead individuals has evolved as the treatment of choice for many patients with end-stage organ disease, but shortage of available donors limits solid-organ transplantation (Rosendale et al., 2003). Despite lots of measures have been taken to enlarge the donor pool, the gap between supply and demand continues to widen (Nathan et al., 2003), and it has become increasingly important to maximize the number of available donors (Bugge, 2009). Brain death diagnosis and detection is the first step toward obtaining a cadaveric organ donor (Cuende et al., 2002). Only 1-4% of hospital deaths and 10% of intensive care unit (ICU) deaths are due to brain death. Few of brain death individuals become organ donors, mainly due to family refusal, medical contraindications, or cardiac arrest. With the aim to increase brain death detection seeking to augment organ and tissue donors, lots of financial, material and human resources have been spent (Mizraji et al., 2009).
The definition of brain death is not identical in many countries. In the USA, brain death is defined as irreversible and complete loss