Tab-Cox: An Interpretable Deep Survival Analysis Model for Patients With Nasopharyngeal Carcinoma Based on TabNet.

The nutritional status of cancer patients is closely associated with the clinical progression of the disease. A survival analysis model combined with a neural network can predict future disease trends in patients, facilitating early prevention and assisting physicians in making diagnoses. However, the complexity of neural networks and their incompatibility with medical tabular data can reduce the interpretability of the model. To address this issue, thr paper propose a novel survival analysis model called Tab-Cox, which combines TabNet and Cox models. This model is specifically designed to predict the survival outcomes of patients with nasopharyngeal carcinoma. The model utilizes TabNet's sequential attention mechanism to extract more interpretable features, providing an interpretable method for identifying disease risk factors. Consequently, the model ensures accurate survival prediction while also making the results more comprehensible for both patients and doctors. The paper tested the efficacy of the model by conducting experiments on various diverse datasets in comparison with other commonly used survival models. The results showed that the proposed model delivered the highest or second-highest accuracy across all datasets. Furthermore, the paper conducted a comparative interpretability analysis against the classical Cox model. In addition and compare the interpretability of the Tab-Cox model with the classical Cox model and discuss the advantages and disadvantages of its interpretability. This demonstrates that Tab-Cox can assist doctors in identifying risk factors that are challenging to capture using artificial methods.

KFPredict: An ensemble learning prediction framework for diabetes based on fusion of key features.

BACKGROUND AND OBJECTIVE: Diabetes is a disease that requires early detection and early treatment, and complications are likely to occur in late stages of the disease, threatening the life of patients. Therefore, in order to diagnose diabetic patients as early as possible, it is necessary to establish a model that can accurately predict diabetes. METHODOLOGY: This paper proposes an ensemble learning framework: KFPredict, which combines multi-input models with key features and machine learning algorithms. We first propose a multi-input neural network model (KF_NN) that fuses key features and uses a decision tree-based selection recursive feature elimination algorithm and correlation coefficient method to screen out the key feature inputs and secondary feature inputs in the model. We then ensemble KF_NN with three machine learning algorithms (i.e., Support Vector Machine, Random Forest and K-Nearest Neighbors) for soft voting to form our predictive classifier for diabetes prediction. RESULTS: Our framework demonstrates good prediction results on the test set with a sensitivity of 0.85, a specificity of 0.98, and an accuracy of 93.5%. Compared with the single prediction method KFPredict, the accuracy is up to 18.18% higher. Concurrently, we also compared KFPredict with the existing prediction methods. It still has good prediction performance, and the accuracy rate is improved by up to 14.93%. CONCLUSION: This paper constructs a diabetes prediction framework that combines multi-input models with key features and machine learning algorithms. Taking tthe PIMA diabetes dataset as the test data, the experiment shows that the framework presents good prediction results.

A novel mesoporous silica nanosphere matrix for the immobilization of proteins and their applications as electrochemical biosensor.

A mesoporous silica nanoshpere (MSN) was proposed to modify glassy carbon electrode (GCE) for the immobilization of protein. Using glucose oxidase (GOD) as a model, direct electrochemistry of protein and biosensing at the MSN modified GCE was studied for the first time. The MNS had large surface area and offered a favorable microenvironment for facilitating the direct electron transfer between enzyme and electrode surface. Scanning electron microscopy, transmission electron microscopy, UV-vis spectroscopy and cyclic voltammetry were used to examine the interaction between GOD and the MSN matrix. The results demonstrated that the immobilized enzyme on the MSN retained its native structure and bioactivity. In addition, the electrochemical reaction showed a surface controlled, reversible two-proton and two-electron transfer process with the apparent electron transfer rate constant of 3.96 s(-1). The MNS-based glucose biosensor exhibited the two linear ranges of 0.04-2.0 mM and 2.0-4.8 mM, a high sensitivity of 14.5 mA M(-1) cm(-2) and a low detection limit of 0.02 mM at signal-to-noise of 3. The proposed biosensor showed excellent selectivity, good reproducibility, acceptable stability and could be successfully applied in the reagentless detection of glucose in real samples at -0.45 V. The work displayed that mesoporous silica nanosphere provided a promising approach for immobilizing proteins and fabrication of excellent biosensors.