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Literature DB >> 32767073

Artificial intelligence in intensive care medicine.

Muhammad Mamdani^1,2,3, Arthur S Slutsky^4,5.

Abstract

Mesh：

Year: 2020 PMID： 32767073 DOI： 10.1007/s00134-020-06203-2

Source DB: PubMed Journal: Intensive Care Med ISSN： 0342-4642 Impact factor: 17.440

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1 in total

1. Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes: a national multi-programme survey.

Authors: Su Kai Gideon Ooi; Andrew Makmur; Alvin Yong Quan Soon; Stephanie Fook-Chong; Charlene Liew; Soon Yiew Sia; Yong Han Ting; Chee Yeong Lim
Journal: Singapore Med J Date: 2019-11-04 Impact factor: 1.858

1 in total

6 in total

1. Automation to improve lung protection.

Authors: Laura A Buiteman-Kruizinga; Ary Serpa Neto; Marcus J Schultz
Journal: Intensive Care Med Date: 2022-05-20 Impact factor: 41.787

Review 2. The physiological underpinnings of life-saving respiratory support.

Authors: Irene Telias; Laurent J Brochard; Arthur S Slutsky; Luciano Gattinoni; Simone Gattarello; Hannah Wunsch; Detajin Junhasavasdikul; Karen J Bosma; Luigi Camporota; Daniel Brodie; John J Marini
Journal: Intensive Care Med Date: 2022-06-12 Impact factor: 41.787

3. Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach.

Authors: Niranjani Prasad; Aishwarya Mandyam; Corey Chivers; Michael Draugelis; C William Hanson; Barbara E Engelhardt; Krzysztof Laudanski
Journal: J Pers Med Date: 2022-04-20

Artificial intelligence in intensive care medicine.

1. Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes: a national multi-programme survey.

1. Automation to improve lung protection.

Review 2. The physiological underpinnings of life-saving respiratory support.

3. Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach.

4. Ignorance Isn't Bliss: We Must Close the Machine Learning Knowledge Gap in Pediatric Critical Care.

5. Leaving a mark: pressure injury research in the intensive care unit.

6. Prediction algorithm for ICU mortality and length of stay using machine learning.