A Neural Attention Model for Categorizing Patient Safety Events
This work addresses the critical issue of medical error prevention for healthcare systems by improving the categorization of patient safety reports, though it appears incremental as it builds on existing neural methods.
The paper tackled the problem of categorizing patient safety event reports to prevent medical errors by proposing a neural attention model for encoding long sequences, achieving significant improvements over existing methods on two large-scale real-world datasets.
Medical errors are leading causes of death in the US and as such, prevention of these errors is paramount to promoting health care. Patient Safety Event reports are narratives describing potential adverse events to the patients and are important in identifying and preventing medical errors. We present a neural network architecture for identifying the type of safety events which is the first step in understanding these narratives. Our proposed model is based on a soft neural attention model to improve the effectiveness of encoding long sequences. Empirical results on two large-scale real-world datasets of patient safety reports demonstrate the effectiveness of our method with significant improvements over existing methods.