Statistical clues to postoperative blood loss: Moving averages applied to medical data

With the advent of computerized databases, medical data has become easy to accumulate; however, effective use of this data continues to pose significant problems. In other circumstances, smoothing algorithms have been used to uncover non-obvious correlations, trends and relationships in noisy data. We have applied four such algorithms to a large dataset of postoperative blood replacement in cardiopulmonary bypass patients. When applied to this dataset, one of the algorithms proved surprisingly effective. It confirmed several previously observed correlations, and also provided an additional series of counterintuitive and apparently unrelated associations. These associations have been explored in an accompanying paper.