| pubmed-article:14992094 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:14992094 | lifeskim:mentions | umls-concept:C0002903 | lld:lifeskim |
| pubmed-article:14992094 | lifeskim:mentions | umls-concept:C0013964 | lld:lifeskim |
| pubmed-article:14992094 | lifeskim:mentions | umls-concept:C0242406 | lld:lifeskim |
| pubmed-article:14992094 | lifeskim:mentions | umls-concept:C0085559 | lld:lifeskim |
| pubmed-article:14992094 | lifeskim:mentions | umls-concept:C0185125 | lld:lifeskim |
| pubmed-article:14992094 | lifeskim:mentions | umls-concept:C2004457 | lld:lifeskim |
| pubmed-article:14992094 | lifeskim:mentions | umls-concept:C0871935 | lld:lifeskim |
| pubmed-article:14992094 | pubmed:issue | 11 | lld:pubmed |
| pubmed-article:14992094 | pubmed:dateCreated | 2004-3-2 | lld:pubmed |
| pubmed-article:14992094 | pubmed:abstractText | Artificial neural networks (ANN) are constructed to simulate processes of the central nervous system of higher creatures. An ANN consists of a set of processing units (nodes) which simulate neurons and are interconnected via a set of "weights" (analogous to synaptic connections in the nervous system) in a way which allows signals to travel through the network in parallel. The nodes (neurons) are simple computing elements. They accumulate input from other neurons by means of a weighted sum. If a certain threshold is reached the neuron sends information to all other connected neurons otherwise it remains quiescent. One major difference compared with traditional statistical or rule-based systems is the learning aptitude of an ANN. At the very beginning of a training process an ANN contains no explicit information. Then a large number of cases with a known outcome are presented to the system and the weights of the inter-neuronal connections are changed by a training algorithm designed to minimise the total error of the system. A trained network has extracted rules that are represented by the matrix of the weights between the neurons. This feature is called generalisation and allows the ANN to predict cases that have never been presented to the system before. Artificial neural networks have shown to be useful predicting various events. Especially complex, non-linear, and time depending relationships can be modelled and forecasted. Furthermore an ANN can be used when the influencing variables on a certain event are not exactly known as it is the case in financial or weather forecasts. This article aims to give a short overview on the function of ANN and their previous use and possible future applications in anaesthesia, intensive care, and emergency medicine. | lld:pubmed |
| pubmed-article:14992094 | pubmed:language | ger | lld:pubmed |
| pubmed-article:14992094 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:14992094 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:14992094 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:14992094 | pubmed:month | Nov | lld:pubmed |
| pubmed-article:14992094 | pubmed:issn | 0003-2417 | lld:pubmed |
| pubmed-article:14992094 | pubmed:author | pubmed-author:MorinA MAM | lld:pubmed |
| pubmed-article:14992094 | pubmed:author | pubmed-author:EberhartAA | lld:pubmed |
| pubmed-article:14992094 | pubmed:author | pubmed-author:WulfHH | lld:pubmed |
| pubmed-article:14992094 | pubmed:author | pubmed-author:GeldnerGG | lld:pubmed |
| pubmed-article:14992094 | pubmed:author | pubmed-author:EberhartL HLH | lld:pubmed |
| pubmed-article:14992094 | pubmed:author | pubmed-author:TraegerMM | lld:pubmed |
| pubmed-article:14992094 | pubmed:author | pubmed-author:PutzkeCC | lld:pubmed |
| pubmed-article:14992094 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:14992094 | pubmed:volume | 52 | lld:pubmed |
| pubmed-article:14992094 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:14992094 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:14992094 | pubmed:pagination | 1055-61 | lld:pubmed |
| pubmed-article:14992094 | pubmed:dateRevised | 2006-11-15 | lld:pubmed |
| pubmed-article:14992094 | pubmed:meshHeading | pubmed-meshheading:14992094... | lld:pubmed |
| pubmed-article:14992094 | pubmed:meshHeading | pubmed-meshheading:14992094... | lld:pubmed |
| pubmed-article:14992094 | pubmed:meshHeading | pubmed-meshheading:14992094... | lld:pubmed |
| pubmed-article:14992094 | pubmed:meshHeading | pubmed-meshheading:14992094... | lld:pubmed |
| pubmed-article:14992094 | pubmed:meshHeading | pubmed-meshheading:14992094... | lld:pubmed |
| pubmed-article:14992094 | pubmed:meshHeading | pubmed-meshheading:14992094... | lld:pubmed |
| pubmed-article:14992094 | pubmed:year | 2003 | lld:pubmed |
| pubmed-article:14992094 | pubmed:articleTitle | [Artificial neural networks. Theory and applications in anesthesia, intensive care and emergency medicine]. | lld:pubmed |
| pubmed-article:14992094 | pubmed:affiliation | Klinik für Innere Medizin, Kreiskrankenhaus Günzburg. | lld:pubmed |
| pubmed-article:14992094 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:14992094 | pubmed:publicationType | English Abstract | lld:pubmed |
| pubmed-article:14992094 | pubmed:publicationType | Review | lld:pubmed |
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| http://linkedlifedata.com/r... | pubmed:referesTo | pubmed-article:14992094 | lld:pubmed |
| http://linkedlifedata.com/r... | pubmed:referesTo | pubmed-article:14992094 | lld:pubmed |
