Fig. 1

Analysis of information processing during decision-making. Methods from the framework of information theory are applied to quantify the differential contribution of patient characteristics to binary decision-making (yes/no). Shannon's entropy [15] of the likelihood distribution with regard to a specific decision is considered a measure of information used for that decision. Zero entropy indicates maximum information and minimum uncertainty. Note that the concept of entropy is related to that of variance for some types of distributions. In scenario A, the distribution of a continuous patient characteristic (e.g. age) does not change in response to the decision. Thus, the likelihood distribution is constant (uniform) and this characteristic is considered uninformative for that decision. Scenario B depicts a characteristic that partially contributes to decision-making. The extent of this contribution is measured by the entropy of the (non-uniform) likelihood distribution. In scenario C, the discrete patient characteristic is decisive, i.e. uncertainty is resolved by maximum information about categories