Kalman filtering in the problem of noise reduction in the absorption spectra of exhaled air

We examined possibilities of the Kalman filter for reducing the noise effects in the analysis of absorption spectra of gas samples, in particular, for samples of the exhaled air. It has been shown that when comparing groups of patients with broncho-pulmonary diseases on the basis of the absorption spectra analysis of exhaled air samples the data preprocessing with the Kalman filtering can improve the classification sensitivity using a support vector kernel with mpl

Classification of patients with broncho-pulmonary diseases based on analysis of absorption spectra of exhaled air samples with SVM and neural network algorithm application

In this work results of classification of patients with broncho-pulmonary diseases based on analysis of exhaled air samples are presented. These results obtained by application of laser photoacoustic spectroscopy method and intellectual data analysis ones (Principal Component Analysis, Support vector machines, neural networks). Absorption spectra of exhaled air of gathered volunteers were registered; data preparation for classification procedure of absorption spectra of exhaled air of healthy and sick people was made. Also error matrices for neural networks and sensitivity/specificity values in case of classification with SVM method were obtained. This work was partially supposed by the Federal Target Program for Research and Development, Contract No. 14.578.21.0082 (unique identifier of applied scientific research and experimental development RFMEFI57814X0082)

Predictive models for COVID-19 detection using routine blood tests and machine learning

The problem of accurate, fast, and inexpensive COVID-19 tests has been urgent till now. Standard COVID-19 tests need high-cost reagents and specialized laboratories with high safety requirements, are time-consuming. Data of routine blood tests as a base of SARS-CoV-2 invasion detection allows using the most practical medicine facilities. But blood tests give general information about a patient’s state, which is not directly associated with COVID-19. COVID-19-specific features should be selected from the list of standard blood characteristics, and decision-making software based on appropriate clinical data should be created. This review describes the abilities to develop predictive models for COVID-19 detection using routine blood tests and machine learning

Application of multiphoton imaging and machine learning to lymphedema tissue analysis

The results of in-vivo two-photon imaging of lymphedema tissue are presented. The study involved 36 image samples from II stage lymphedema patients and 42 image samples from healthy volunteers. The papillary layer of the skin with a penetration depth of about 100 μm was examined. Both the collagen network disorganization and increase of the collagen/elastin ratio in lymphedema tissue, characterizing the severity of fibrosis, was observed. Various methods of image characterization, including edge detectors, a histogram of oriented gradients method, and a predictive model for diagnosis using machine learning, were used. The classification by “ensemble learning” provided 96% accuracy in validating the data from the testing set