Abstract: Techniques are described that employ a machine learning approach for detecting data discrepancies during clinical data integration. In an embodiment, a computer implemented method comprises receiving historical clinical data messages converted from a native format to a target format via a mapping function that maps different sets of historical data elements included in the historical clinical data messages into defined data description paths, and training anomaly detection models for each of the defined data description paths to characterize normal characteristics of the different sets of historical data elements for each of the defined data description path. The method further comprises receiving new clinical data messages converted from the native format or to the target format via the mapping function, and detecting abnormal characteristics of different sets of new data elements for corresponding data description paths of the defined data description paths using the anomaly detection models.