Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation.

Behrooz MamandipoorFernando Frutos-VivarOscar PeñuelasRichard RezarKonstantinos RaymondosAlfonso MurielBin DuArnaud W ThilleFernando RíosMarco GonzálezLorenzo Del-SorboMaria Del Carmen MarínBruno Valle PinheiroMarco Antonio SoaresNicolas NinSalvatore M MaggioreAndrew BerstenMalte KelmRaphael Romano BrunoPravin AminNahit CakarGee Young SuhFekri AbrougManuel JibajaDimitros MatamisAmine Ali ZeggwaghYuda SutherasanAntonio AnzuetoBernhard WernlyAndrés EstebanChristian JungVenet Osmani

Published in: BMC medical informatics and decision making (2021)

NCT02731898 ( https://clinicaltrials.gov/ct2/show/NCT02731898 ), prospectively registered on April 8, 2016.

Keyphrases

machine learning
image quality
computed tomography
cardiovascular events
dual energy
contrast enhanced
artificial intelligence
respiratory failure
big data
mechanical ventilation
positron emission tomography
magnetic resonance imaging
deep learning
intensive care unit
cardiovascular disease
magnetic resonance