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Sepsis is an overwhelming immune response to an infection that is triggered by pathogens. Immune cells are released into the blood to fight the infection, which can lead to blood clots and leaky blood vessels. It is a major public health issue that impacts 1.7 million adults in the United States and kills 270,000. Despite heterogeneity in sepsis, physicians typically use a “one-size-fits-all” treatment of antibiotics and fluids that do not account for patient differences.

Dr Fohner adapted a text-mining tool that used a machine-learning algorithm to search for trends in medications and procedures administered within the first 24 hours of hospitalisation to 29,543 septic patients. This data was extracted from the electronic health record (EHR), a digital patient record that provides physicians with patient summaries but was designed for insurance billing. Each medication, diagnosis, and procedure in the EHR has a code that allows insurance companies to effectively charge for administered services. Dr Fohner extracted codes for medications, orders, and procedures administered to the study patients. 

The data was organised to account for the frequency of certain billing codes per patient record. Subsequently, a machine learning algorithm took all patient data and generated 42 topics that were combinations of EHR codes that commonly occur together. The algorithm also identified dominant topics per patient. Creating such subsets of patient types will help researchers create more unique treatment plans based on pre-existing disease patterns. 

Source: The Daily of the University of Washington