Neural Networks classification Tutorial
Signal detection based on spontaneous reporting involves looking for any new patterns or seemingly significant new findings in the safety database. It may be one or more reports showing particularly strong evidence of a previously unknown adverse reaction for that drug, or involving adverse events that are usually caused by drugs, such as aplastic anaemia or toxic epidermal necrolysis. More often, it is a matter of looking for patterns or clusters of reports that stand out from the background. These clusters may be identified by looking at data tables, or – for large databases, such as those held by regulatory authorities or by a company with product with many cases reported – using computerised methods involving statistical disproportion. Examples include the Proportional Reporting Ratio, Bayesian Combination Propagation Neural Network used by the WHO Uppsala Monitoring Centre and the Modified Gamma Poisson Shrinker method used by FDA and MHRA.
Having identified potential tentative signals it is necessary to evaluate them and to consider whether they are real or not. Often, an apparent signal can result just from the disease that the drug is treating – so if a drug is used in patients for the treatment of high blood pressure, it would not be surprising to find reports of high blood pressure itself, and also kidney disease, stroke and heart failure, because these are either contributory or complications of high blood pressure. This is often a difficult process: evaluating signals involves looking at the individual case reports and assessing the strength of causation of the drug in each case. Other sources of safety data are usually also checked at this time – for example, the data from clinical trials and toxicology studies.
Neural Networks by Satish Kumar B01_0234
Book (McGraw Hill Higher Education)
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Your child is expected to be2011-03-21 05:40:32 by dixiessmom
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