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Results for "prognostic system"

Clinical prediction system of complications among COVID-19 patients: a development and validation retrospective multicentre study

arXiv · Nov 28

A retrospective study in Abu Dhabi, UAE, developed a machine learning-based prognostic system to predict the risk of seven complications in COVID-19 patients using data from 3,352 patient encounters. The system, trained on data from the first 24 hours of admission, achieved high accuracy (AUROC > 0.80) in predicting complications like AKI, ARDS, and elevated biomarkers in geographically split test sets. The models primarily used gradient boosting and logistic regression.

AI for prognoses in cancer care: Integrating physician expertise with deep learning

MBZUAI · Invalid Date

MBZUAI researchers developed Human-in-the-Loop for Prognosis (HuLP), a new AI system designed to help physicians assess cancer progression by providing information about its predictions and allowing user intervention. The system aims to foster collaboration between physicians and AI, rather than replacing doctors. It was presented at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Why it matters: This research highlights the potential of AI to augment physician expertise in critical areas like cancer prognosis, improving patient care and treatment decisions.

Mosques Smart Domes System using Machine Learning Algorithms

arXiv · Aug 30

This paper proposes a smart dome system for mosques that uses machine learning to automatically control dome ventilation based on weather conditions and outside temperatures. The system was tested on the Prophet Mosque in Saudi Arabia using K-Nearest Neighbors and Decision Tree algorithms. The Decision Tree algorithm achieved a higher accuracy of 98% compared to 95% for the k-NN algorithm.

Forecasting hospitalizations with AI

MBZUAI · Invalid Date

MBZUAI Professor Agathe Guilloux developed the SigLasso model to forecast hospitalizations using real-time data from Google and Météo France during the COVID-19 pandemic. The model integrates mobility data and weather patterns to predict hospitalization rates 10-14 days in advance. SigLasso outperformed industry standards like GRU and Neural CDE in reducing reconstruction error. Why it matters: This research demonstrates the potential of AI to improve healthcare resource allocation and crisis management by accurately predicting patient flow using readily available data.