Table 1.

Primary literature on machine learning applied to pulmonary and critical care medicine

Topic	Title	Authorship	Year
Pulmonary	A comprehensive immunohistochemistry algorithm for the histological subtyping of small biopsies obtained from non-small cell lung cancers	Koh et al.	2014
	Computerized analysis of telemonitored respiratory sounds for predicting acute exacerbations of COPD	Fernandez-Granero et al.	2015
	Automated interpretation of pulmonary function tests in adults with respiratory complaints	Topalovic et al.	2017
	Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks	Lakhani et al.	2017
	Improving prediction of risk of hospital admission in chronic obstructive pulmonary disease: application of machine learning to telemonitoring data	Orchard et al.	2018
	Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists	Rajpurkar et al.	2018
	Automatic pulmonary nodule detection applying deep learning or machine learning algorithms to the LIDC-IDRI database: a systematic review	Pehrson et al.	2019
	Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT	Uthoff et al.	2019
	End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography	Ardila et al.	2019
	Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests	Topalovic et al.	2019
Critical care	Presymptomatic prediction of sepsis in intensive care unit patients	Lukaszewski et al.	2008
	Prediction of severe sepsis using SVM model	Wang et al.	2010
	Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach	Awad et al.	2017
	An interpretable machine learning model for accurate prediction of sepsis in the ICU	Nemati et al.	2018
	Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement	Parreco et al.	2018
	An artificial neural network model for predicting successful extubation in intensive care units	Hsieh et al.	2018
	The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care	Komorowski et al.	2018
	Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis	Seymour et al.	2019
	A machine learning approach for predicting urine output after fluid administration	Lin et al.	2019
	Developing well-calibrated illness severity scores for decision support in the critically ill	Cosgriff et al.	2019