Construction and validation of risk prediction models for pulmonary embolism in hospitalized patients based on different machine learning methods

ObjectiveThis study aims to apply different machine learning (ML) methods to construct risk prediction models for pulmonary embolism (PE) in hospitalized patients, and to evaluate and compare the predictive efficacy and clinical benefit of each model.MethodsWe conducted a retrospective study involving 332 participants (172 PE positive cases and 160 PE negative cases) recruited from Guangdong Medical University. Participants were randomly divided into a training group (70%) and a validation group (30%). Baseline data were analyzed using univariate analysis, and potential independent risk factors associated with PE were further identified through univariate and multivariate logistic regression analysis. Six ML models, namely Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), and AdaBoost were developed. The predictive efficacy of each model was compared using the receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC). Clinical benefit was assessed using decision curve analysis (DCA).ResultsLogistic regression analysis identified lower extremity deep venous thrombosis, elevated D-dimer, shortened activated partial prothrombin time, and increased red blood cell distribution width as potential independent risk factors for PE. Among the six ML models, the RF model achieved the highest AUC of 0.778. Additionally, DCA consistently indicated that the RF model offered the greatest clinical benefit.ConclusionThis study developed six ML models, with the RF model exhibiting the highest predictive efficacy and clinical benefit in the identification and prediction of PE occurrence in hospitalized patients

Quasi-Matrix and Quasi-Inverse-Matrix Projective Synchronization for Delayed and Disturbed Fractional Order Neural Network

This paper is concerned with the quasi-matrix and quasi-inverse-matrix projective synchronization between two nonidentical delayed fractional order neural networks subjected to external disturbances. First, the definitions of quasi-matrix and quasi-inverse-matrix projective synchronization are given, respectively. Then, in order to realize two types of synchronization for delayed and disturbed fractional order neural networks, two sufficient conditions are established and proved by constructing appropriate Lyapunov function in combination with some fractional order differential inequalities. And their estimated synchronization error bound is obtained, which can be reduced to the required standard as small as what we need by selecting appropriate control parameters. Because of the generality of the proposed synchronization, choosing different projective matrix and controllers, the two synchronization types can be reduced to some common synchronization types for delayed fractional order neural networks, like quasi-complete synchronization, quasi-antisynchronization, quasi-projective synchronization, quasi-inverse projective synchronization, quasi-modified projective synchronization, quasi-inverse-modified projective synchronization, and so on. Finally, as applications, two numerical examples with simulations are employed to illustrate the efficiency and feasibility of the new synchronization analysis

Bio-Inspired Aramid Fibers@silica Binary Synergistic Aerogels with High Thermal Insulation and Fire-Retardant Performance

Flame-retardant, thermal insulation, mechanically robust, and comprehensive protection against extreme environmental threats aerogels are highly desirable for protective equipment. Herein, inspired by the core (organic)-shell (inorganic) structure of lobster antenna, fire-retardant and mechanically robust aramid fibers@silica nanocomposite aerogels with core-shell structures are fabricated via the sol-gel-film transformation and chemical vapor deposition process. The thickness of silica coating can be well-defined and controlled by the CVD time. Aramid fibers@silica nanocomposite aerogels show high heat resistance (530 °C), low thermal conductivity of 0.030 W·m−1·K−1, high tensile strength of 7.5 MPa and good flexibility. More importantly, aramid fibers@silica aerogels have high flame retardancy with limiting oxygen index 36.5. In addition, this material fabricated by the simple preparation process is believed to have potential application value in the field of aerospace or high-temperature thermal protection

Synergistic Effect in Plasmonic CuAu Alloys as Co-Catalyst on SnIn₄S₈ for Boosted Solar-Driven CO₂ Reduction

The photoreduction of CO2 to chemical fuels represents a promising technology to mitigate the current energy dilemma and global warming problems. Unfortunately, the original photocatalysts suffer from many side reactions and a poor CO2 conversion efficiency. The rational combination of active co-catalyst with pristine photocatalysts for promoting the adsorption and activation of CO2 is of vital importance to tackle this grand challenge. Herein, we rationally designed a SnIn4S8 nanosheet photocatalyst simultaneously equipped with CuAu alloys. The experimental results proved that the CuAu alloy can trap the electrons and enhance the separation and transport efficiency of the photogenerated carrier in the photocatalyst, alleviating the kinetical difficulty of the charge transfer process because of the preferable localized surface plasmon resonance (LSPR). Furthermore, the CuAu alloy works as the synergistic site to increase the CO2 adsorption and activation capacity. The optimized CuAu-SnIn4S8 photocatalyst exhibited a superior performance with CO generation rates of 27.87 μmol g−1 h−1 and CH4 of 7.21 μmol g−1 h−1, which are about 7.6 and 2.5 folds compared with SnIn4S8. This work highlights the critical role of alloy cocatalysts in boosting a CO2 activation and an efficient CO2 reduction, thus contributing to the development of more outstanding photocatalytic systems