Predicting Acute Kidney Injury In Sepsis Patients: A Machine Learning Approach - Laurell,Patrik; Lidman,Dwight (2022)
Sepsis-induced Acute Kidney Injury (AKI) is a common cause of death for sepsis patients, yet it is poorly understood by the medical community. Using Tandem Mass Spectrometry (MS/MS), the blood plasma of 135 sepsis patients has been analyzed to measure protein levels. In this thesis we will present an exploratory application of machine learning on this data, with the intent of producing a model which can reliably predict AKI. MS/MS is an expensive analytical technique which produces high precision data of high dimensionality, which thus contains large amounts of information but is difficult to produce. We present our approach to preprocessing, feature selection and model selection when dealing with small amounts of high-dimensional biological data from Mass Spectrometry (MS). In our investigation we conclude that, with the amount and quality of data currently available, it is not possible at this time to predict AKI in sepsis patients using machine learning. However, a new potential biomarker for AKI is presented, which performs better than established clinical markers on our data set.
Keywords: machine learning; sepsis