Machine Learning for Real-Time Prediction of Sepsis Onset
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Keywords

Sepsis
Machine Learning
Real-Time Prediction
Early Intervention

How to Cite

[1]
Viktor Petrov, “Machine Learning for Real-Time Prediction of Sepsis Onset”, Journal of AI in Healthcare and Medicine, vol. 3, no. 1, pp. 1–8, Apr. 2023, Accessed: Nov. 12, 2024. [Online]. Available: https://healthsciencepub.com/index.php/jaihm/article/view/12

Abstract

Sepsis is a life-threatening condition that requires early intervention for improved patient outcomes. Machine learning (ML) algorithms have shown promise in predicting sepsis onset, but real-time prediction remains a challenge. This study develops ML algorithms for real-time prediction of sepsis onset, aiming to enable early intervention. Using a dataset of patient records, various ML models are trained and evaluated for their predictive performance. The results demonstrate the feasibility of real-time sepsis prediction using ML, highlighting the potential impact on patient care and outcomes.

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References

Reddy, Byrapu, and Surendranadha Reddy. "Evaluating The Data Analytics For Finance And Insurance Sectors For Industry 4.0." Tuijin Jishu/Journal of Propulsion Technology 44.4 (2023): 3871-3877.

Venigandla, Kamala, et al. "Leveraging AI-Enhanced Robotic Process Automation for Retail Pricing Optimization: A Comprehensive Analysis." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.2 (2023): 361-370.

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