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Cloud-Enabled Predictive Analytics for Assessing Post-Surgical Complication Risks Using AI Models
0
Zitationen
6
Autoren
2025
Jahr
Abstract
A cloud-based predictive analytic system that uses AI models to predict post-surgery complications is described in this paper. The system predicts sepsis, hemorrhage, and lung issues using Random Forest, XGBoost, LSTM, and Transformer. Latency is low because data intake and doctor alerts are done in real time. After accounting for AIdriven early warnings, death and ICU admissions decreased. The model also showed that it was demographically neutral, allowing it to make reasonable forecasts. These findings show that AI-driven applications could fill post-surgical care gaps and improve patient outcomes by aiding crucial care decisionmaking.
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