Data drift detection for unstructured texts via deep learning autoencoders

A computer-implemented method, comprising:

• accessing, by a device operatively coupled to a processor, a pre-trained natural language model and a set of unstructured text reports on which the pre-trained natural language model is to be executed;
• determining, by the device and via execution of a deep learning autoencoder, how different a first set of reconstruction errors associated with the set of unstructured text reports are from a second set of reconstruction errors associated with a set of training unstructured text reports on which the pre-trained natural language model was trained;
• and
• generating, by the device and in response to a determination that the first set of reconstruction errors differ from the second set of reconstruction errors by more than a threshold margin, a first alert indicating that data drift has occurred and that the pre-trained natural language model is thereby unable to confidently analyze the set of unstructured text reports.

A computer program product for facilitating data drift detection for unstructured texts via deep learning autoencoders, the computer program product comprising a non-transitory computer-readable memory having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

• access a pre-trained clinical natural language model and a set of unstructured clinical text reports on which the pre-trained clinical natural language model is to be executed;
• determine, via execution of a deep learning autoencoder, how different a first set of reconstruction errors associated with the set of unstructured clinical text reports are from a second set of reconstruction errors associated with a set of training unstructured clinical text reports on which the pre-trained clinical natural language model was trained;
• and
• generate, in response to a determination that the first set of reconstruction errors differ from the second set of reconstruction errors by more than a threshold margin, a first alert indicating that data drift has occurred and that the pre-trained clinical natural language model is thereby unable to confidently analyze the set of unstructured clinical text reports.