Predicting critical health outcomes such as patient mortality and hospital readmission is essential for improving survivability. However, healthcare datasets have many concurrences that create complexities, leading to poor predictions. Consequently, pre-processing the data is crucial to improve its quality. In this study, we use an existing pre-processing algorithm, concatenation, to improve data quality by decreasing the complexity of datasets. Sixteen healthcare datasets were extracted from two databases - MIMIC III and University of Illinois Hospital - converted to the event logs, they were then fed into the concatenation algorithm. The pre-processed event logs were then fed to the Split Miner (SM) algorithm to produce a process model. Process model quality was evaluated before and after concatenation using the following metrics: fitness, precision, F-Measure, and complexity. The pre-processed event logs were also used as inputs to the Decay Replay Mining (DREAM) algorithm to predict critical outcomes. We compared predicted results before and after applying the concatenation algorithm using Area Under the Curve (AUC) and Confidence Intervals (CI). Results indicated that the concatenation algorithm improved the quality of the process models and predictions of the critical health outcomes.

通过使用连接算法进行预处理来提高数据质量，本研究从两个数据库中提取了16个医疗数据集，将其转换为事件日志，使用Split Miner（SM）算法生成了一个过程模型，并使用Decay Replay Mining（DREAM）算法预测关键结果，结果表明连接算法改善了过程模型和预测的关键健康结果的质量。

基于过程挖掘的预处理步骤在关键健康结果预测中的影响