Early Identification of Patients at Risk of Sepsis in a Hospital Environment

HIGHLIGHTS

Early identification of sepsis despite low-quality and sparse data.

New strategy to identify cases due to the underreporting with ICD-10 codes.

Recommendation of more frequent data collection and with better quality.

Identification of variables that most contribute to the system prediction.

Abstract

Sepsis is a systematic response to an infectious disease, being a concerning factor because of the increase in the mortality ratio for every delayed hour in the identification and start of patient’s treatment. Studies that aim to identify sepsis early are valuable for the healthcare domain. Further, studies that propose machine learning-based models to identify sepsis risk are scarce for the Brazilian scenario. Hence, we propose the early identification of sepsis considering data from a Brazilian hospital. We developed a temporal series based on LSTM to predict sepsis in patients considering a three-day timestep. The patients were selected using both criteria, ICD-10, and qSOFA, where we supplemented qSOFA with the additional identification of words referring to infections in the clinical texts. Additionally, we tested a Random Forest classifier to classify patients with sepsis with a single timestep before the sepsis event, evaluating the most relevant features. We achieved an accuracy of 0.907, a sensitivity of 0.912, and a specificity of 0.971 when considering a three-day timestep with LSTM. The Random Forest classifier achieved an accuracy of 0.971, a sensitivity of 0.611, and a specificity of 0.998. The features age, blood glucose, systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, and admission days had the most influence over the algorithm classification, with age being the most relevant feature. We achieved satisfactory results compared with the literature considering a scenario of spaced measures and a high amount of missing data.

Keywords:
sepsis; machine learning; healthcare