Chen CY, et al. - Researchers undertook this case-control analysis to introduce a breath test for ventilator-associated pneumonia (VAP) with a standardized protocol for a machine learning technique. Participants were intensive care unit admissions of a hospital in southern Taiwan from February 2017 to June 2019. Cases included patients with VAP (n = 33) and controls were ventilated patients without pneumonia (n = 26). Experts obtained exhaled breath and examined the electric resistance alterations of 32 sensor arrays of an electronic nose. They divided the data into a set for training algorithms and a set for testing. Employing eight machine learning algorithms, the mean accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and the area under the receiver operator characteristic curves in the testing set were 0.81 ± 0.04, 0.79 ± 0.08, 0.83 ± 0.00, 0.85 ± 0.02, 0.77 ± 0.06, and 0.85 ± 0.04, respectively. Findings revealed good accuracy in identifying VAP by sensor array and machine learning techniques. Artificial intelligence can potentially serve as an aid to the physician for making a clinical diagnosis. Clear protocols for data processing and the modeling procedure required to enhance generalizability.