Exploratory study on classification of chronic obstructive pulmonary disease combining multi-stage feature fusion and machine learning

Abstract

Background Due to the complexity and high heterogeneity of the acute exacerbation of chronic obstructive pulmonary disease (AECOPD), the guidelines (global initiative for chronic obstructive, GOLD) is unable to fully guide the treatment of AECOPD. Objectives To provide a rapid treatment in line with the development of the AECOPD after admission. In this paper, we propose a multi-stage feature fusion (MSFF) framework combining machine learning to track the diseases deterioration risk of the AECOPD. Methods First, we identify 408 AECOPD patients as the study population. Then, feature segment and fusion methods are applied to generate the phased data set. Finally, human studies are designed to evaluate the performance of the MSFF framework. Results The experimental results show that the proposed framework is potential to obtain the full-process tracking of deterioration risk for the AECOPD patients. The proposed MSFF framework achieves a higher overall accuracy average and F1 scores than the four physician groups i.e., IM, Surgery, Emergency, and ICU. Conclusions The proposed MSFF model may serve as a useful disease tracking tool to estimate the deterioration risk at each stage, and finally achieve the disease monitoring and management for AECOPD patients.

A machine learning based approach for quantitative evaluation of cell migration in Transwell assays based on deformation characteristics

“…In recent years, artificial intelligence (AI) techniques have emerged as powerful tools for automated, rapid cancer cell analysis without the need of human intervention. 11 Thus, a novel method without the cell removal operation is needed that combines AI with the Transwell migration assay. Identification of the cell phenotypic state (whether the cell is migrated or not) is automatically a key step in AI techniques.…”