AIMC Topic: Sputum

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Qualitative analysis of biological tuberculosis samples by an electronic nose-based artificial neural network.

The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease Jul 1, 2017
OBJECTIVE: To apply an e-nose system for monitoring headspace volatiles in biological samples from Egyptian patients with active pulmonary tuberculosis (TB) and healthy controls (HCs) and compare them with standard sputum analysis.
Humans Neural Networks, Computer Female Aged Young Adult Adult Sensitivity and Specificity Male Middle Aged Case-Control Studies Adolescent Socioeconomic Factors Mass Screening Principal Component Analysis Egypt Sputum Tuberculosis, Pulmonary Electronic Nose
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Unsupervised learning technique identifies bronchiectasis phenotypes with distinct clinical characteristics.

The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease Mar 1, 2016
BACKGROUND: Unsupervised learning technique allows researchers to identify different phenotypes of diseases with complex manifestations.
Humans Anti-Bacterial Agents Phenotype Prospective Studies Female Risk Factors Aged Adult Male Middle Aged Quality of Life Cohort Studies Body Mass Index Follow-Up Studies Cluster Analysis Unsupervised Machine Learning Cough Sputum Forced Expiratory Volume Pseudomonas aeruginosa Bronchiectasis
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