An evolutionary machine learning for pulmonary hypertension animal model from arterial blood gas analysis

Wang, Mingjing; Zhou, Tao; Lv, Shushu; Shi, Beibei; Chen, Siyuan; Heidari, Ali Asghar; Huang, Xiaoying; Chen, Huiling; Wang, Liangxing; Wu, Peiliang

doi:10.1016/j.compbiomed.2022.105529

Cited by 14 publications

(6 citation statements)

References 104 publications

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“…In this subsection, we present some of the analytical evaluation methods used to analyze the results of the feature selection experiments ( Hu et al, 2022a , b ; Liu et al, 2022a , c ; Shan et al, 2022 ; Shi et al, 2022 ; Xia et al, 2022a ; Yang et al, 2022b ; Ye et al, 2022 ). The aim is to provide a valid theoretical basis for demonstrating that the bCOWOA-KELM model performs better in feature selection than other comparative models.…”

Section: Experiments Results and Analysismentioning

confidence: 99%

“…Moreover, the performance advantages of the method for feature selection were successfully validated in 24 UCI data sets. Shi et al (2022) combined multiple strategies integrated slime mold algorithm (MSSMA) with KELM technology and successfully proposed a predictive model (MSSMA-KELM) that can be used to predict pulmonary hypertension. El-Kenawy et al (2020) proposed a feature selection algorithm (SFS-Guided WOA) that combined with well-known classifiers (KNN, SVM, etc.)…”

Section: Introductionmentioning

confidence: 99%

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Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine

Zhao²,

Liu³

et al. 2022

Front. Neuroinform.

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Intradialytic hypotension (IDH) is an adverse event occurred during hemodialysis (HD) sessions with high morbidity and mortality. The key to preventing IDH is predicting its pre-dialysis and administering a proper ultrafiltration prescription. For this purpose, this paper builds a prediction model (bCOWOA-KELM) to predict IDH using indices of blood routine tests. In the study, the orthogonal learning mechanism is applied to the first half of the WOA to improve the search speed and accuracy. The covariance matrix is applied to the second half of the WOA to enhance the ability to get out of local optimum and convergence accuracy. Combining the above two improvement methods, this paper proposes a novel improvement variant (COWOA) for the first time. More, the core of bCOWOA-KELM is that the binary COWOA is utilized to improve the performance of the KELM. In order to verify the comprehensive performance of the study, the paper sets four types of comparison experiments for COWOA based on 30 benchmark functions and a series of prediction experiments for bCOWOA-KELM based on six public datasets and the HD dataset. Finally, the results of the experiments are analyzed separately in this paper. The results of the comparison experiments prove fully that the COWOA is superior to other famous methods. More importantly, the bCOWOA performs better than its peers in feature selection and its accuracy is 92.41%. In addition, bCOWOA improves the accuracy by 0.32% over the second-ranked bSCA and by 3.63% over the worst-ranked bGWO. Therefore, the proposed model can be used for IDH prediction with future applications.

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Section: Experiments Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine

Zhao²,

Liu³

et al. 2022

Front. Neuroinform.

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“…These findings are consistent with the lung ultrasonography scores and anatomical observations, corroborating previous clinical retrospective studies and laboratory research on blast injuries ( Zhang et al, 2015 ; Yuanbo et al, 2016 ; Tong et al, 2018 ; Smith and Garner, 2019 ; April et al, 2021 ; Carius et al, 2022 ; Al-Hajj et al, 2023 ). Previous studies have demonstrated that prompt and effective arterial blood gas analysis following BLI is crucial for diagnosing conditions and developing treatment plans ( Manera et al, 2020 ; Xue et al, 2020 ; Chong et al, 2021 ; Shi et al, 2022 ). However, early-stage conditions may lack the necessary means for timely, continuous, and effective arterial blood gas monitoring.…”

Section: Discussionmentioning

confidence: 99%

Esophageal pressure monitoring and its clinical significance in severe blast lung injury

Shao,

Wu,

Wang

et al. 2024

Front. Bioeng. Biotechnol.

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BackgroundThe incidence of blast lung injury (BLI) has been escalating annually due to military conflicts and industrial accidents. Currently, research into these injuries predominantly uses animal models. Despite the availability of various models, there remains a scarcity of studies focused on monitoring respiratory mechanics post-BLI. Consequently, our objective was to develop a model for monitoring esophageal pressure (Pes) following BLI using a biological shock tube (BST), aimed at providing immediate and precise monitoring of respiratory mechanics parameters post-injury.MethodsSix pigs were subjected to BLI using a BST, during which Pes was monitored. We assessed vital signs; conducted blood gas analysis, hemodynamics evaluations, and lung ultrasound; and measured respiratory mechanics before and after the inflicted injury. Furthermore, the gross anatomy of the lungs 3 h post-injury was examined, and hematoxylin and eosin staining was conducted on the injured lung tissues for further analysis.ResultsThe pressure in the experimental section of the BST reached 402.52 ± 17.95 KPa, with a peak pressure duration of 53.22 ± 1.69 ms. All six pigs exhibited an anatomical lung injury score ≥3, and pathology revealed classic signs of severe BLI. Post-injury vital signs showed an increase in HR and SI, along with a decrease in MAP (p < 0.05). Blood gas analyses indicated elevated levels of Lac, CO2-GAP, A-aDO2, HB, and HCT and reduced levels of DO2, OI, SaO2, and OER (p < 0.05). Hemodynamics and lung ultrasonography findings showed increased ELWI, PVPI, SVRI, and lung ultrasonography scores and decreased CI, SVI, GEDI, and ITBI (p < 0.05). Analysis of respiratory mechanics revealed increased Ppeak, Pplat, Driving P, MAP, PEF, Ri, lung elastance, MP, Ptp, Ppeak − Pplat, and ΔPes, while Cdyn, Cstat, and time constant were reduced (p < 0.05).ConclusionWe have successfully developed a novel respiratory mechanics monitoring model for severe BLI. This model is reliable, repeatable, stable, effective, and user-friendly. Pes monitoring offers a non-invasive and straightforward alternative to blood gas analysis, facilitating early clinical decision-making. Our animal study lays the groundwork for the early diagnosis and management of severe BLI in clinical settings.

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“…The single- and multi-objective Economic and Emission Dispatch (EED) issues may be solved using an enhanced version of the SMA known as the ISMA, which also considers the impacts of valve points [ 37 ]. ISMA was designed to improve upon the performance of SMA.…”

Section: Methods Of Smamentioning

confidence: 99%

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Gharehchopogh

Uçan

İbrikçi

et al. 2023

Arch Computat Methods Eng

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Meta-heuristic algorithms have a high position among academic researchers in various fields, such as science and engineering, in solving optimization problems. These algorithms can provide the most optimal solutions for optimization problems. This paper investigates a new meta-heuristic algorithm called Slime Mould algorithm (SMA) from different optimization aspects. The SMA algorithm was invented due to the fluctuating behavior of slime mold in nature. It has several new features with a unique mathematical model that uses adaptive weights to simulate the biological wave. It provides an optimal pathway for connecting food with high exploration and exploitation ability. As of 2020, many types of research based on SMA have been published in various scientific databases, including IEEE, Elsevier, Springer, Wiley, Tandfonline, MDPI, etc. In this paper, based on SMA, four areas of hybridization, progress, changes, and optimization are covered. The rate of using SMA in the mentioned areas is 15, 36, 7, and 42%, respectively. According to the findings, it can be claimed that SMA has been repeatedly used in solving optimization problems. As a result, it is anticipated that this paper will be beneficial for engineers, professionals, and academic scientists.

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An evolutionary machine learning for pulmonary hypertension animal model from arterial blood gas analysis

Cited by 14 publications

References 104 publications

Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine

Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine

Esophageal pressure monitoring and its clinical significance in severe blast lung injury

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

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