Abstract: A named-entity recognition (NER) model detects named entities with types that correspond to protected health information (PHI) in potentially sensitive documents. The NER model is trained to detect named entities corresponding to both personally identifiable information (PII) and medical terms. Output of the NER model is preprocessed as input to a random forest classifier that outputs a verdict that documents comprise sensitive data. The verdict is interpretable via high confidence named entities detected by the NER model that led to the verdict.

Abstract: A clustering-based pipeline is trained with training data to generate clusters of the labeled training data and yield a trained clustering model. Previously unseen data or unlabeled data is input into the trained clustering-based pipeline for the trained clustering model to determine cluster memberships of the unseen/unlabeled data. The trained clustering-based pipeline then selects from the unlabeled data for labeling based on cluster membership, including based on non-cluster membership or being out-of-distribution (OOD) with respect to the clusters. The trained clustering-based pipeline samples at different sampling sizes depending on whether embeddings are cluster members or OOD. The sampling will favor the OOD embeddings to provide more of the unlabeled data that corresponds to the OOD embeddings for labeling in order to improve or enrich training data.

Abstract: A named-entity recognition (NER) model detects named entities with types that correspond to protected health information (PHI) in potentially sensitive documents. The NER model is trained to detect named entities corresponding to both personally identifiable information (PII) and medical terms. Output of the NER model is preprocessed as input to a random forest classifier that outputs a verdict that documents comprise sensitive data. The verdict is interpretable via high confidence named entities detected by the NER model that led to the verdict.