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  1. Article ; Online: A dynamic nomogram for predicting 28-day mortality in septic shock: a Chinese retrospective cohort study.

    Xu, Zhijun / Huang, Man

    PeerJ

    2024  Volume 12, Page(s) e16723

    Abstract: Background: Septic shock is a severe life-threatening disease, and the mortality of septic shock in China was approximately 37.3% that lacks prognostic prediction model. This study aimed to develop and validate a prediction model to predict 28-day ... ...

    Abstract Background: Septic shock is a severe life-threatening disease, and the mortality of septic shock in China was approximately 37.3% that lacks prognostic prediction model. This study aimed to develop and validate a prediction model to predict 28-day mortality for Chinese patients with septic shock.
    Methods: This retrospective cohort study enrolled patients from Intensive Care Unit (ICU) of the Second Affiliated Hospital, School of Medicine, Zhejiang University between December 2020 and September 2021. We collected patients' clinical data: demographic data and physical condition data on admission, laboratory data on admission and treatment method. Patients were randomly divided into training and testing sets in a ratio of 7:3. Univariate logistic regression was adopted to screen for potential predictors, and stepwise regression was further used to screen for predictors in the training set. Prediction model was constructed based on these predictors. A dynamic nomogram was performed based on the results of prediction model. Using receiver operator characteristic (ROC) curve to assess predicting performance of dynamic nomogram, which were compared with Sepsis Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation II (APACHE II) systems.
    Results: A total of 304 patients with septic shock were included, with a 28-day mortality of 25.66%. Systolic blood pressure, cerebrovascular disease, Na, oxygenation index (PaO
    Conclusion: The dynamic nomogram for predicting 28-day mortality in Chinese patients with septic shock may help physicians to assess patient survival and optimize personalized treatment strategies for septic shock.
    MeSH term(s) Humans ; Nomograms ; Retrospective Studies ; ROC Curve ; Sepsis ; Shock, Septic/therapy
    Language English
    Publishing date 2024-01-23
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2703241-3
    ISSN 2167-8359 ; 2167-8359
    ISSN (online) 2167-8359
    ISSN 2167-8359
    DOI 10.7717/peerj.16723
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Recurrent malignant hyperthermia after scoliosis correction surgery.

    Su, Junfeng / Huang, Man

    World journal of emergency medicine

    2023  Volume 15, Issue 1, Page(s) 70–72

    Language English
    Publishing date 2023-10-06
    Publishing country China
    Document type Journal Article
    ZDB-ID 2753264-1
    ISSN 1920-8642
    ISSN 1920-8642
    DOI 10.5847/wjem.j.1920-8642.2024.006
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  3. Article ; Online: Intensive care unit-acquired weakness: Recent insights.

    Chen, Juan / Huang, Man

    Journal of intensive medicine

    2023  Volume 4, Issue 1, Page(s) 73–80

    Abstract: Intensive care unit-acquired weakness (ICU-AW) is a common complication in critically ill patients and is associated with a variety of adverse outcomes. These include the need for prolonged mechanical ventilation and ICU stay; higher ICU, in-hospital, ... ...

    Abstract Intensive care unit-acquired weakness (ICU-AW) is a common complication in critically ill patients and is associated with a variety of adverse outcomes. These include the need for prolonged mechanical ventilation and ICU stay; higher ICU, in-hospital, and 1-year mortality; and increased in-hospital costs. ICU-AW is associated with multiple risk factors including age, underlying disease, severity of illness, organ failure, sepsis, immobilization, receipt of mechanical ventilation, and other factors related to critical care. The pathological mechanism of ICU-AW remains unclear and may be considerably varied. This review aimed to evaluate recent insights into ICU-AW from several aspects including risk factors, pathophysiology, diagnosis, and treatment strategies; this provides new perspectives for future research.
    Language English
    Publishing date 2023-08-30
    Publishing country China
    Document type Journal Article ; Review
    ISSN 2667-100X
    ISSN (online) 2667-100X
    DOI 10.1016/j.jointm.2023.07.002
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  4. Article ; Online: Prediction of successful weaning from renal replacement therapy in critically ill patients based on machine learning.

    Liang, Qiqiang / Xu, Xin / Ding, Shuo / Wu, Jin / Huang, Man

    Renal failure

    2024  Volume 46, Issue 1, Page(s) 2319329

    Abstract: Background: Predicting the successful weaning of acute kidney injury (AKI) patients from renal replacement therapy (RRT) has emerged as a research focus, and we successfully built predictive models for RRT withdrawal in patients with severe AKI by ... ...

    Abstract Background: Predicting the successful weaning of acute kidney injury (AKI) patients from renal replacement therapy (RRT) has emerged as a research focus, and we successfully built predictive models for RRT withdrawal in patients with severe AKI by machine learning.
    Methods: This retrospective single-center study utilized data from our general intensive care unit (ICU) Database, focusing on patients diagnosed with severe AKI who underwent RRT. We evaluated RRT weaning success based on patients being free of RRT in the subsequent week and their overall survival. Multiple logistic regression (MLR) and machine learning algorithms were adopted to construct the prediction models.
    Results: A total of 976 patients were included, with 349 patients successfully weaned off RRT. Longer RRT duration (7.0
    Conclusion: High-risk factors for unsuccessful RRT weaning in severe AKI patients include prolonged RRT duration. Machine learning prediction models, when compared to models based on multivariate logistic regression using these indicators, offer distinct advantages in predictive accuracy.
    MeSH term(s) Humans ; Critical Illness/therapy ; Retrospective Studies ; Weaning ; Renal Replacement Therapy ; Acute Kidney Injury/therapy ; Machine Learning
    Language English
    Publishing date 2024-02-28
    Publishing country England
    Document type Journal Article
    ZDB-ID 632949-4
    ISSN 1525-6049 ; 0886-022X
    ISSN (online) 1525-6049
    ISSN 0886-022X
    DOI 10.1080/0886022X.2024.2319329
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article: Editorial: Advances in extracorporeal life support in critically ill patients, volume III.

    Tu, Guo-Wei / Dobrilovic, Nikola / Huang, Man / Luo, Zhe

    Frontiers in medicine

    2024  Volume 11, Page(s) 1394830

    Language English
    Publishing date 2024-03-26
    Publishing country Switzerland
    Document type Editorial
    ZDB-ID 2775999-4
    ISSN 2296-858X
    ISSN 2296-858X
    DOI 10.3389/fmed.2024.1394830
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  6. Article ; Online: Graph-Based Unsupervised Feature Selection for Interval-Valued Information System.

    Xu, Weihua / Huang, Man / Jiang, Zongying / Qian, Yuhua

    IEEE transactions on neural networks and learning systems

    2023  Volume PP

    Abstract: Feature selection has become one of the hot research topics in the era of big data. At the same time, as an extension of single-valued data, interval-valued data with its inherent uncertainty tend to be more applicable than single-valued data in some ... ...

    Abstract Feature selection has become one of the hot research topics in the era of big data. At the same time, as an extension of single-valued data, interval-valued data with its inherent uncertainty tend to be more applicable than single-valued data in some fields for characterizing inaccurate and ambiguous information, such as medical test results and qualified product indicators. However, there are relatively few studies on unsupervised attribute reduction for interval-valued information systems (IVISs), and it remains to be studied how to effectively control the dramatic increase of time cost in feature selection of large sample datasets. For these reasons, we propose a feature selection method for IVISs based on graph theory. Then, the model complexity could be greatly reduced after we utilize the properties of the matrix power series to optimize the calculation of the original model. Our approach can be divided into two steps. The first is feature ranking with the principles of relevance and nonredundancy, and the second is selecting top-ranked attributes when the number of features to keep is fixed as a priori. In this article, experiments are performed on 14 public datasets and the corresponding seven comparative algorithms. The results of the experiments verify that our algorithm is effective and efficient for feature selection in IVISs.
    Language English
    Publishing date 2023-04-17
    Publishing country United States
    Document type Journal Article
    ISSN 2162-2388
    ISSN (online) 2162-2388
    DOI 10.1109/TNNLS.2023.3263684
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  7. Article ; Online: Application of mNGS to describe the clinical and microbial characteristics of severe burn a tanker explosion at a tertiary medical center: a retrospective study patients following.

    Wu, Jing / Huang, Man

    BMC infectious diseases

    2021  Volume 21, Issue 1, Page(s) 1086

    Abstract: Background: Multiple organ dysfunction syndrome secondary to infection is the leading cause of death in burn patients. Bloodstream infection (BSI) and the prognosis of burn patients are negatively correlated. Metagenomic next-generation sequencing (mNGS) ...

    Abstract Background: Multiple organ dysfunction syndrome secondary to infection is the leading cause of death in burn patients. Bloodstream infection (BSI) and the prognosis of burn patients are negatively correlated. Metagenomic next-generation sequencing (mNGS) can detect many potential pathogens and may be more valuable for patients with severe burns.
    Methods: We retrospectively explored the utility of mNGS in describing the clinical and microbial characteristics of severely burned patients with BSI. We compared mNGS with blood culture.
    Results: Fourteen patients (127 blood samples) developed 71 episodes of BSIs with 102 unique causative pathogens. The median total body surface area was 93%. The overall 90-day mortality was 43%. In total, 17 (23.9%) episodes were polymicrobial, and 61 (86.1%) episodes originated from the wound. In total, 62/71 cases (87%) showed positive findings by mNGS, while 42/71 cases (59%) showed positive findings using blood culture. We found that mNGS outperformed culture, especially in terms of fungi (27% vs. 6%, p < 0.0001).
    Conclusions: The incidence of BSI and polymicrobial in patients with large-area severe burns is high. mNGS has potential value in the diagnosis of fungal infections and coinfections in such patients. In addition, mNGS may provide unique guidance for antibiotic therapy in complicated BSI.
    MeSH term(s) Burns/complications ; Explosions ; High-Throughput Nucleotide Sequencing ; Humans ; Retrospective Studies ; Sensitivity and Specificity
    Language English
    Publishing date 2021-10-21
    Publishing country England
    Document type Journal Article
    ISSN 1471-2334
    ISSN (online) 1471-2334
    DOI 10.1186/s12879-021-06790-5
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  8. Article ; Online: Prediction of carbapenem-resistant gram-negative bacterial bloodstream infection in intensive care unit based on machine learning.

    Liang, Qiqiang / Ding, Shuo / Chen, Juan / Chen, Xinyi / Xu, Yongshan / Xu, Zhijiang / Huang, Man

    BMC medical informatics and decision making

    2024  Volume 24, Issue 1, Page(s) 123

    Abstract: Background: Predicting whether Carbapenem-Resistant Gram-Negative Bacterial (CRGNB) cause bloodstream infection when giving advice may guide the use of antibiotics because it takes 2-5 days conventionally to return the results from doctor's order.: ... ...

    Abstract Background: Predicting whether Carbapenem-Resistant Gram-Negative Bacterial (CRGNB) cause bloodstream infection when giving advice may guide the use of antibiotics because it takes 2-5 days conventionally to return the results from doctor's order.
    Methods: It is a regional multi-center retrospective study in which patients with suspected bloodstream infections were divided into a positive and negative culture group. According to the positive results, patients were divided into the CRGNB group and other groups. We used the machine learning algorithm to predict whether the blood culture was positive and whether the pathogen was CRGNB once giving the order of blood culture.
    Results: There were 952 patients with positive blood cultures, 418 patients in the CRGNB group, 534 in the non-CRGNB group, and 1422 with negative blood cultures. Mechanical ventilation, invasive catheterization, and carbapenem use history were the main high-risk factors for CRGNB bloodstream infection. The random forest model has the best prediction ability, with AUROC being 0.86, followed by the XGBoost prediction model in bloodstream infection prediction. In the CRGNB prediction model analysis, the SVM and random forest model have higher area under the receiver operating characteristic curves, which are 0.88 and 0.87, respectively.
    Conclusions: The machine learning algorithm can accurately predict the occurrence of ICU-acquired bloodstream infection and identify whether CRGNB causes it once giving the order of blood culture.
    MeSH term(s) Humans ; Machine Learning ; Intensive Care Units ; Carbapenems/pharmacology ; Male ; Middle Aged ; Female ; Retrospective Studies ; Aged ; Gram-Negative Bacterial Infections/drug therapy ; Bacteremia/microbiology ; Gram-Negative Bacteria/drug effects ; Gram-Negative Bacteria/isolation & purification ; Adult ; Anti-Bacterial Agents/pharmacology ; Drug Resistance, Bacterial
    Language English
    Publishing date 2024-05-14
    Publishing country England
    Document type Journal Article ; Multicenter Study
    ZDB-ID 2046490-3
    ISSN 1472-6947 ; 1472-6947
    ISSN (online) 1472-6947
    ISSN 1472-6947
    DOI 10.1186/s12911-024-02504-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Book ; Online: Soft-SVM Regression For Binary Classification

    Huang, Man / Carvalho, Luis

    2022  

    Abstract: The binomial deviance and the SVM hinge loss functions are two of the most widely used loss functions in machine learning. While there are many similarities between them, they also have their own strengths when dealing with different types of data. In ... ...

    Abstract The binomial deviance and the SVM hinge loss functions are two of the most widely used loss functions in machine learning. While there are many similarities between them, they also have their own strengths when dealing with different types of data. In this work, we introduce a new exponential family based on a convex relaxation of the hinge loss function using softness and class-separation parameters. This new family, denoted Soft-SVM, allows us to prescribe a generalized linear model that effectively bridges between logistic regression and SVM classification. This new model is interpretable and avoids data separability issues, attaining good fitting and predictive performance by automatically adjusting for data label separability via the softness parameter. These results are confirmed empirically through simulations and case studies as we compare regularized logistic, SVM, and Soft-SVM regressions and conclude that the proposed model performs well in terms of both classification and prediction errors.

    Comment: 13pages,8figures
    Keywords Statistics - Machine Learning ; Computer Science - Machine Learning
    Subject code 310
    Publishing date 2022-05-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: A review on settlement models of municipal solid waste landfills

    Ren, Yinbang / Zhang, Zhenying / Huang, Man

    Waste Management. 2022 July, v. 149 p.79-95

    2022  

    Abstract: Currently, landfill is the most common, economical, and convenient method for municipal solid waste (MSW) disposal in countries around the world. MSW has a complex composition and special engineering characteristics, which lead to a very complex ... ...

    Abstract Currently, landfill is the most common, economical, and convenient method for municipal solid waste (MSW) disposal in countries around the world. MSW has a complex composition and special engineering characteristics, which lead to a very complex settlement mechanism in MSW landfills. This article reviews the description of this settlement mechanism in the existing literature and classifies it into stress-related mechanisms, biodegradation processes of organic substances, water-related mechanisms and physical and chemical processes of inorganic components. Based on the settlement mechanism, the influencing factors of landfill settlement were analysed, including the composition of MSW, physical parameters, environmental factors, and the operation mode of the landfill. Some practical engineering suggestions are obtained by analysing the influencing factors of MSW landfill settlement. Four commonmethods for studying the settlement of MSW landfills are presented, including laboratory experiments, in-situ settlement monitoring, theoretical analysis, and numerical simulation. We classified the existing settlement models into six categories: a soil mechanics, rheological, empirical, biodegradation, constitutive, and multiphase coupling models. Advantages and disadvantages of the different models and their applicability are compared and analysed. Moreover, limitations in the modelling process of MSW landfill settlement and future research directions are discussed.
    Keywords biodegradation ; landfills ; mathematical models ; municipal solid waste ; soil mechanics ; waste management ; Landfill ; Settlement ; Settlement mechanism ; Influencing factors ; Settlement models
    Language English
    Dates of publication 2022-07
    Size p. 79-95.
    Publishing place Elsevier Ltd
    Document type Article ; Online
    ZDB-ID 2001471-5
    ISSN 1879-2456 ; 0956-053X
    ISSN (online) 1879-2456
    ISSN 0956-053X
    DOI 10.1016/j.wasman.2022.06.019
    Database NAL-Catalogue (AGRICOLA)

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