Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning
Fig 1
Pipeline for natural language processing and prediction.
Our algorithm first takes as input a triage note and processes it by applying tokenization followed by bigram and negation detection, the latter using a customized version of the NegEx tool [14]. The processed text is then transformed into a set of features. The Bag-of-Words features count how many times each word in our vocabulary appears in the processed note, and the Topic model features (derived using the Mallet [17] tool) measure how much certain topics are represented in the note. A Support Vector Machine (SVM) is then trained on these sets of features to determine whether the patient presents an infection, using the SVMperf software [15].