Machine learning for intensive care units

Abstract

Intensive Care Units (ICUs) collect vast amounts of data, including patient vitals which are often recorded as time series data. This data can be analysed to extract meaningful data, which can aid in timely intervention, improving patient outcomes. The complexity and quantity of this data also makes it impossible to analyze manually. Traditional statistical methods are not adequate enough to extract important information and recognize patterns. Deep neural network based language models however, excel at recognizing patterns. This project leverages this to train a BERT based [2] transformer for Masked Language Modelling (MLM) [1] and Next Sentence Prediction (NSP) tasks. Procuring the embeddings from this transformer can aid in further research in this topic.