Hierarchy-aware Adverse Reaction Embeddings for Signal Detection.

Post-market drug surveillance monitors new and evolving treatments for their effectiveness and safety in real-world conditions. A large amount of drug safety surveillance data is captured by spontaneous reporting systems such as the FAERS. Developing automated methods to identify actionable safety signals from these databases is an active area of research.

Reducing Physicians' Cognitive Load During Chart Review: A Problem-Oriented Summary of the Patient Electronic Record.

Overabundance of information within electronic health records (EHRs) has resulted in a need for automated systems to mitigate the cognitive burden on physicians utilizing today's EHR systems. We present ProSPER, a Problem-oriented Summary of the Patient Electronic Record that displays a patient summary centered around an auto-generated problem list and disease-specific views for chronic conditions. ProSPER was developed using 1,500 longitudinal patient records from two large multi-specialty medical groups in the United States, and leverages multiple natural language processing (NLP) components targeting various fundamental (e.

Extraction of Information Related to Drug Safety Surveillance From Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-Aware Neural Attentive Models.

Background: An adverse drug event (ADE) is commonly defined as "an injury resulting from medical intervention related to a drug." Providing information related to ADEs and alerting caregivers at the point of care can reduce the risk of prescription and diagnostic errors and improve health outcomes. ADEs captured in structured data in electronic health records (EHRs) as either coded problems or allergies are often incomplete, leading to underreporting.

Adverse Drug Events Detection in Clinical Notes by Jointly Modeling Entities and Relations Using Neural Networks.

Background And Significance: Adverse drug events (ADEs) occur in approximately 2-5% of hospitalized patients, often resulting in poor outcomes or even death. Extraction of ADEs from clinical narratives can accelerate and automate pharmacovigilance. Using state-of-the-art deep-learning neural networks to jointly model concept and relation extraction, we achieved the highest integrated task score in the 2018 Medication and Adverse Drug Event (MADE) 1.

A convolutional route to abbreviation disambiguation in clinical text.

Objective: Abbreviations sense disambiguation is a special case of word sense disambiguation. Machine learning methods based on neural networks showed promising results for word sense disambiguation (Festag and Spreckelsen, 2017) [1] and, here we assess their effectiveness for abbreviation sense disambiguation.

Methods: Convolutional Neural Network (CNN) models were trained, one for each abbreviation, to disambiguate abbreviation senses.