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Electronic Health Records (EHRs) give a lot of information regarding a patient's progress in health, who is admitted to an Intensive Care Unit (ICU). Sepsis is a critical condition suffered by a patient who, if not treated in a timely manner can cause casualties. Machine learning algorithms have evolved to utilize EHRs to help doctors detect the onset of sepsis. In this work, we present a random forest-based ensemble machine learning technique to work on patient data, also called vital sign input, from ICU. The data we used is published as a part of the Physionet Challenge 2019 [11]. The proposed technique performs well on data that contain a major chunk as missing values due to the sparsity of measurement taken in an ICU. We used a combined classifier and an early predictor approach to accomplish the task. The classifier does the job of classification when the early prediction is not possible due to a lack of data. While early predictor predicts the onset of sepsis based on the patient's information it received from previous recordings of vital sign inputs. A utility metric score is used to evaluate the early predictor. The score increases with early predictions and decreases with late predictions as well as false alarms. Our team named &#x2018;Tricog' finished 58th in the challenge with a utility score of 0.149 in the official phase on the full test set data.

Early Prediction of Sepsis: Using State-of-the-art Machine Learning Techniques on Vital Sign Inputs