Rapid evaluation of human biomonitoring data using pattern recognition systems.

Assessing human biomonitoring data often necessitates dealing with fragmentary prior knowledge and a complex set of variables. A procedure for explorative data analysis via decision-tree analysis was undertaken to obtain high-level descriptive summary information on human exposure on a timely basis. This study is based on a subset of monitoring data of the Environmental Specimen Bank for Human Tissues within the German Environmental Specimen Bank (n sigma: 2401: 42/58% males/females; 34/66% born in East/West Germany).