A Machine Learning-Based Algorithm for Early Detection of Sepsis in Hospitalized Patients: Development and Evaluation
Venkata Raghuveer Burugadda, Priyanka Makrand Mane, Abhishek Kumar, Neha Bhati
Abstract
Early sepsis detection improves patient outcomes and care. This research provides a Machine Learning (ML) system for hospitalized sepsis detection. Gradient boosting, an ensemble learning method, analyses patient data to detect sepsis early. A comprehensive electronic health record database, MIMIC-III, was used to design and test the algorithm. The algorithm's sepsis detection accuracy, precision, recall, F1 score, and ROC AUC were measured. The proposed approach was more accurate than traditional models. It accurately predicted sepsis patients and aid treatment. Real-time clinical decision-making is possible with the algorithm's fast prediction and training. It could revolutionize sepsis management by giving doctors a dependable early detection and intervention tool. The algorithm must be tested in various healthcare environments and patient demographics. To implement this technology widely, privacy and ethics must be addressed. The approach may improve patient outcomes and lower healthcare costs by detecting sepsis early.