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Detection of potential patient harms from clinical notes in electronic healthcare records: The Shakespeare Method

Many methods for finding adverse events in the text of healthcare records rely on predefining potential adverse events before searching for prespecified words and phrases or manual labeling (standardization) by investigators.

We developed the Shakespeare Method to identify potential adverse events, even if unknown or unattributed, without any pre-specifications or standardization of notes from electronic healthcare records.

The method was inspired by word-frequency analysis methods used to uncover the true authorship of disputed works credited to William Shakespeare. In this talk I will cover the details of the Shakespeare Method where we use a combination of filtering, classification, and topic modeling to identify documents from a specific group or time of interest, to review for potential adverse events.

Data Augmentation for Sequence Labeling. A Case Study in Food Parsing

Deep Learning supervised models are ubiquitously sought in the tech industry, where data and classification use cases abound. While established tech companies...