This paper is published in Volume-8, Issue-5, 2023
Area
Machine Learning
Author
Likitha Reddy Ganta, Varshitha Obili, S. C. Asma Afrin, Rahul Gajula, T. Vishnu Vardhan Reddy, T. N. Ranganatham
Org/Univ
Annamacharya Institute of Technology and Sciences, Rajampet, Andhra Pradesh, India
Pub. Date
03 January, 2023
Paper ID
V8I5-1287
Publisher
Keywords
Decision Tree, Random forest Classifier, SVM, Logistic Regression

Citationsacebook

IEEE
Likitha Reddy Ganta, Varshitha Obili, S. C. Asma Afrin, Rahul Gajula, T. Vishnu Vardhan Reddy, T. N. Ranganatham. Next Generation Risk Stratification of ICU Patients using Machine Learning, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Likitha Reddy Ganta, Varshitha Obili, S. C. Asma Afrin, Rahul Gajula, T. Vishnu Vardhan Reddy, T. N. Ranganatham (2023). Next Generation Risk Stratification of ICU Patients using Machine Learning. International Journal of Advance Research, Ideas and Innovations in Technology, 8(5) www.IJARIIT.com.

MLA
Likitha Reddy Ganta, Varshitha Obili, S. C. Asma Afrin, Rahul Gajula, T. Vishnu Vardhan Reddy, T. N. Ranganatham. "Next Generation Risk Stratification of ICU Patients using Machine Learning." International Journal of Advance Research, Ideas and Innovations in Technology 8.5 (2023). www.IJARIIT.com.

Abstract

Personalized Remote patient monitoring in the intensive care unit (ICU) is a critical observation and assessment duty required for precision medicine. We recently developed a cloud-based intelligent. We use a remote patient monitoring (IRPM) architecture that is state-of-the-art in risk classification. The most accurate prediction is made using machine learning, but with few characteristics that rely on vital indicators. Physiological characteristics gathered within and outside of hospitals are widely employed. We made a big contribution in this effort. enhance the basic IRPM framework's capability by developing three machine learning models for Measurements of readmission, abnormalities, and next-day vital signs We give a formally represented version of a feature engineering algorithm, as well as the construction and validation of three replicable machine learning algorithms ICU patient readmission, anomaly, and next-day vitals prediction models.