A Less Traditional Approach to Biomedical Signal Processing for Sepsis Prediction

Abstract

Most of the data generated by monitors in a clinical setting represent time series data which can be visualized and subsequently used for decision making. This usually is the simplest part. A more challenging aspect is using this data for more complex task like machine learning with the same goal – computer assisted decisions. Within this challenge raw biomedical signal data need to be preprocessed before being passed to the machine learning algorithm. This can be done by a multitude of methods. A number of such methods comes from the field of Algorithmic Complexity and although of a promising nature, these particular methods are poorly explored yet. The current research presents an example of applying the Block Decomposition Method to data routinely generated by patients in a modern Intensive Care Unit. The final goal of a larger research, the actual research being part of, is building a system for early sepsis prediction.