HE USE of information technology for the improvement of patient care by detecting and informing clinicians about key clinical events has already a long history with numerous successful examples in various areas of medicine. 1,2 Often the success of such systems depends on the feasibility of extracting exact rules from existing comprehensive domain knowledge. Thus the interpretation of laboratory results is wellsuited for support by computer systems if the cut-off between normal and critical values is known. Under this precondition the value of automated alerting systems for improving patient care is well-proven. 3,4 Unfortunately, some medical conditions make it impossible to define a normal range for those parameters that are essential in monitoring the respective condition. For kidney transplant recipients a serum creatinine within the “normal” range is not the norm but an exception. Meanwhile, despite ongoing efforts to develop other methods, serum creatinine remains the most important parameter for the assessment of renal graft function. 5‐7 A rise in serum creatinine corresponds to a deterioration in graft function. The attending physician has to recognize “significant” increases in serum creatinine that warrant further diagnostic measures to exclude or verify an underlying graft rejection which requires immediate therapy to prevent graft damage or loss. Whether a new measurement constitutes a rise can only be determined in relation to at least one previous measurement. Because each patient has an individual range of “usual” creatinine values with an individual size of the “usual” changes between consecutive measurements, the decision whether a rise in creatinine is “significant” still requires experience and intuition. Exact rules that define the properties of a “critical” sequence of creatinine values are not available because the pathophysiology of transplant rejection is still incompletely understood. Simple algorithms or rule-based expert systems are therefore not an option for the development of diagnostic decision support systems for this problem. Instead a technique that is capable of dealing with sequences (time series) of low-frequency measurements with unequal distances in between is required. Experience with assessment of time series already exists in other domains with applications ranging from stock prices over meteorological data to electrocardiograms. These applications work by pattern recognition (ie, a new time series is compared to stored patterns). But traditional algorithms for comparison of time series like Euclidian distance or arithmetic correlation are based on the assumption of equidistant measurements or equal length of the time series. Both assumptions are not met by laboratory results and moreover the measurements from blood sampes are usually rather infrequent. An approach that is not limited by these assumptions is dynamic time warping (DTW) that has been successfully applied to pattern recognition in time series. 8 One of its original application domains is speech recognition, where the matching of spoken words to word templates requires an algorithm that allows for different timing and pronunciation. The result of DTW can be seen as a “warping” of the time axis such that the distance between two time series becomes minimal with respect to a distance function. The cumulative value of this function yields a measure of distance or— because the two concepts are dual—similarity. When an incomplete domain theory prohibits the a priori definition of ideal patterns, it is still possible to compare new problems to historical cases. Case-based reasoning (CBR) is a promising approach with existing applications in a number of fields including medicine. 9 The idea is to mimic the human technique of problem-solving by analogy. To solve a new problem, the system retrieves similar stored cases and uses the solutions associated with these cases to generate a solution for the new problem. The cognitive adequacy to the human expert’s way of solving problems is one of the advantages of CBR in the medical domain. 9 The crucial task in the development of a CBR system is the definition of a similarity measure for the case retrieval. To our knowledge, so far no similarity measure for the comparison of courses of laboratory values by CBR has been established. We perform this study to evaluate whether DTW is a similarity measure that permits the application of CBR to time series of laboratory values, thus improving the assessment of creatinine courses in kidney transplant recipients.