Diagnostic Value of Model-Based Iterative Algorithm in Tuberculous Pleural Effusion. (9th February 2022)

• Record Type: Journal Article
• Authors: Xi, Suya; Sun, Jinhao; Wang, Hongjing; Qiao, Qingzhe; He, Xianghong
• Other Names: Khan Rahim Academic Editor.
• Is Part Of: Journal of healthcare engineering. Volume 2022(2022)
• Publication Date: 2022-02-09
• Subjects: Hospital buildings -- Environmental engineering -- Periodicals; Medical technology -- Periodicals; Medical informatics -- Periodicals; 610.28
• DOI: 10.1155/2022/7845767
• Languages: English
• ISSNs: 2040-2295

Abstract

Abstract : Although there are several diagnostic modalities for tuberculous pleurisy, there is still a lack of easy, cost-effective, and rapid methods for confirming the diagnosis. In order to facilitate clinicians to diagnose patients with tuberculous pleurisy at an early stage, help patients to obtain treatment early, and reduce lung damage, it is hoped that new techniques will be available in the future to help diagnose tuberculous pleurisy rapidly in the clinic. To this end, this paper investigates the problem of bidirectional consistency based on event-triggered iterative learning. Firstly, a dynamic linearized data model of TB pleurisy intelligent system is established using compact-form dynamic linearization method, and a parameter estimation algorithm of TB pleurisy data model is proposed; then, based on this data model, an output observer and a dead zone controller are designed, and an event-triggered distributed model-free iterative learning bidirectional consistency control strategy is constructed by combining with signal graph theory. In this paper, 112 patients with pleural effusion were collected, including 76 patients with confirmed or clinically diagnosed tuberculous pleural effusion and 36 patients with nontuberculous pleural effusion. Pleural effusion T-SPOT.TB, blood T-SPOT.TB, pleural effusion Xpert MTB/RIF, and pleural effusion adenosine deaminase (ADA) tests were performed before treatment in the included patients. The sensitivity of pleural effusion T-SPOT.TB was higher than that of peripheral blood T-SPOT.TB (76.32%, 58/76), pleural effusion Xpert MTB/RIF (65.79%, 50/76), and pleural effusion ADA (28.95%, 22/76); the differences were statistically significant ( x 2 = 14.74, 25.22, and 76.45, P < 0.01). The specificity of the Xpert MTB/RIF test for pleural effusion (100%, 36/36) was higher than that for pleural effusion T-SPOT.TB (77.78%, 28/36), peripheral blood T-SPOT.TB, and pleural effusion T-SPOT.TB. The sensitivity of the combined Xpert MTB/RIF test (64.47%, 49/76) was lower than that of the pleural effusion T-SPOT.TB alone (97.37%, 74/76).