Abstract:
Sepsis is a potentially fatal illness that needs to be identified quickly in order to enhance
patient outcomes. Unfortunately, because of its non-specific symptoms, early detection
can be difficult. Predictive models for early sepsis diagnosis could be developed using
the wealth of data provided by electronic health records (EHRs). By examining EHR
data and finding patterns linked to the onset of sepsis, machine learning (ML)
algorithms have demonstrated encouraging promise in this field. The purpose of this
project is to use EHR data to create and assess a machine learning (ML) predictive
model for early sepsis detection. Our main goal will be to extract pertinent features—
such as demographics, vital signs, test findings, and medication information—from
easily accessible EHR data. To determine which machine learning technique performs
best in terms of accuracy, sensitivity, and specificity, we will analyze and contrast a
number of different models, including logistic regression, support vector machines, and
random forests. Our model's performance will be compared to conventional sepsis
scoring methods, and it will be assessed on a retrospective dataset of patients with
confirmed sepsis cases. The ultimate objective of this research is to create a
therapeutically applicable tool that will help medical personnel identify people who are
at danger of sepsis early on. Early interventions, better patient outcomes, and lower
healthcare expenditures can result from this.
