Early Prediction of Sepsis using ML Algorithms on Clinical Data

A poorly controlled host reaction to infection results in sepsis, a potentially fatal organ failure. If not treated early, it can cause multiple organ failure, septic shock and even death. Most of the time, it is a serious infection-related consequence, especially in low and middle-income countries...

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Bibliographic Details
Published in2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) pp. 1 - 8
Main Authors N, Smitha, Singh, Arushi, Ghosh, Abhishek Siddhartha, Kumari, Arya, R, Tanuja, Manjula, S.H, K. R, Venugopal
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.07.2023
Subjects
Buildings
Computational modeling
Electric shock
Heart rate
Pediatrics
Physionet Challenge Dataset
Prediction using ML
Predictive models
Sepsis
Septic shock
Training
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Summary:A poorly controlled host reaction to infection results in sepsis, a potentially fatal organ failure. If not treated early, it can cause multiple organ failure, septic shock and even death. Most of the time, it is a serious infection-related consequence, especially in low and middle-income countries where it is a significant contributor to maternal and newborn morbidity and mortality. We are building a model which will predict Sepsis well before time using only vital signs of the patients like Heart Rate, Age, BP, and o2stat level. We have proposed a system that will predict the occurrence of sepsis well before time to save a patient's life. We will be using Physionet 2019 Challenge dataset for training our ML model to predict the desired output.
ISSN:2473-7674
DOI:10.1109/ICCCNT56998.2023.10307995