Extracting adverse reactions to medications or treatments is a crucial activity in the biomedical domain. The task involves identifying mentions of drugs and their adverse effects/events in raw text, which is challenging due to the unstructured nature of clinical narratives. In this paper, we propose TpT-ADE, a novel joint two-phase transformer model combined with natural language processing (NLP) techniques, to identify adverse events (AEs) caused by drugs. In the first phase of TpT-ADE, entities are extracted and are grounded with their standard terms using the Unified Medical Language System (UMLS) knowledge base. In the second phase, entity and relation classification is performed to determine the presence of a relationship between the drug and AE pairs. TpT-ADE also identifies the intensity of AE entities by constructing a parts-of-speech (POS) embedding model. Unlike previous approaches that use complex classifiers, TpT-ADE employs a shallow neural network and yet outperforms the state-of-the-art methods on the standard ADE corpus.

Data security and privacy is an issue of growing importance in the healthcare domain. In this paper, we present an auditing system to detect privacy violations for unstructured text documents such as healthcare records. Given a sensitive document, we present an anomaly detection algorithm that can find the top-k suspicious keyword queries that may have accessed the sensitive document. Since unstructured healthcare data, such as medical reports and query logs, are not easily available for public research, in this paper, we show how one can use the publicly available DBLP data to create an equivalent healthcare data and query log, which can then be used for experimental evaluation.