ObjectiveTo predict the total hospitalization expenses of bronchopneumonia inpatients in a tertiay hospital of Sichuan Province through BP neural network and support vector machine models, and analyze the influencing factors.MethodsThe home page information of 749 cases of bronchopneumonia discharged from a tertiay hospital of Sichuan Province in 2017 was collected and compiled. The BP neural network model and the support vector machine model were simulated by SPSS 20.0 and Clementine softwares respectively to predict the total hospitalization expenses and analyze the influencing factors.ResultsThe accuracy rate of the BP neural network model in predicting the total hospitalization expenses was 81.2%, and the top three influencing factors and their importances were length of hospital stay (0.477), age (0.154), and discharge department (0.083). The accuracy rate of the support vector machine model in predicting the total hospitalization expenses was 93.4%, and the top three influencing factors and their importances were length of hospital stay (0.215), age (0.196), and marital status (0.172), but after stratified analysis by Mantel-Haenszel method, the correlation between marital status and total hospitalization expenses was not statistically significant (χ2=0.137, P=0.711).ConclusionsThe BP neural network model and the support vector machine model can be applied to predicting the total hospitalization expenses and analyzing the influencing factors of patients with bronchopneumonia. In this study, the prediction effect of the support vector machine is better than that of the BP neural network model. Length of hospital stay is an important influencing factor of total hospitalization expenses of bronchopneumonia patients, so shortening the length of hospital stay can significantly lighten the economic burden of these patients.
Objective To determine whether lymph node-targeted chemotherapy with carbon nanoparticles absorbing 5-FU affects expressions of bcl-2, bax and caspase-3 in gastric cancer tissues, metastatic lymph nodes and normal gastric mucosa. Methods Twenty-eight patients with gastric cancer in our department were divided into lymph node-targeted chemotherapy (LNTC) group and control group from October 2005 to August 2006. The patients were treated with carbon nanoparticles absorbing 5-FU before operation in LNTC group and those were operated directly in control group. The gastric cancer tissues, metastatic lymph nodes and normal gastric mucosa were collected after operation. The expressions of bcl-2, bax and caspase-3 in those tissues were determined by immunohistochemical technique. Results In LNTC group, the positive expression rate of bcl-2 in gastric cancer tissues and metastatic lymph nodes was significantly lower than those in control group (28.6% vs . 78.6% , 25.0% vs . 70.0% , P ＜ 0.05), the positive expression rate of bax (85.7% vs . 28.6% , 80.0% vs . 30.0% ) and caspase-3 (57.1% vs . 14.3% , 55.0% vs . 15.0% ) in gastric cancer tissues and metastatic lymph nodes was significantly higher than those in control group ( P ＜ 0.05). The positive expression rate of bcl-2, bax and caspase-3 in normal gastric mucosa was not significantly different between two groups ( P ＞ 0.05). Conclusion The lymph node-targeted chemotherapy with carbon nanoparticles absorbing 5-FU can down-regulate the expression of bcl-2 and up-regulate the expression of bax and caspase-3 in gastric cancer tissues and metastatic lymph nodes, and therefore by affecting the expression levels of these apoptosis molecules may be one of the ways to induce tumor cell apoptosis.
Heart sound signal is a kind of physiological signal with nonlinear and nonstationary features. In order to improve the accuracy and efficiency of the phonocardiogram (PCG) classification, a new method was proposed by means of support vector machine (SVM) in which the complete ensemble empirical modal decomposition with adaptive noise (CEEMDAN) permutation entropy was as the eigenvector of heart sound signal. Firstly, the PCG was decomposed by CEEMDAN into a number of intrinsic mode functions (IMFs) from high to low frequency. Secondly, the IMFs were sifted according to the correlation coefficient, energy factor and signal-to-noise ratio. Then the instantaneous frequency was extracted by Hilbert transform, and its permutation entropy was constituted into eigenvector. Finally, the accuracy of the method was verified by using a hundred PCG samples selected from the 2016 PhysioNet/CinC Challenge. The results showed that the accuracy rate of the proposed method could reach up to 87%. In comparison with the traditional EMD and EEMD permutation entropy methods, the accuracy rate was increased by 18%–24%, which demonstrates the efficiency of the proposed method.
The in-vivo electron paramagnetic resonance (EPR) method can be used for on-site, rapid, and non-invasive detection of radiation dose to casualties after nuclear and radiation emergencies. For in-vivo EPR spectrum analysis, manual labeling of peaks and calculation of signal intensity are often used, which have problems such as large workload and interference by subjective factors. In this study, a method for automatic classification and identification of in-vivo EPR spectra was established using support vector machine (SVM) technology, which can in-batch and automatically identify and screen out invalid spectra due to vibration and dental surface water interference during in-vivo EPR measurements. In this study, a spectrum analysis method based on genetic algorithm optimization neural network (GA-BPNN) was established, which can automatically identify the radiation-induced signals in in-vivo EPR spectra and predict the radiation doses received by the injured. The experimental results showed that the SVM and GA-BPNN spectrum processing methods established in this study could effectively accomplish the automatic spectra classification and radiation dose prediction, and could meet the needs of dose assessment in nuclear emergency. This study explored the application of machine learning methods in EPR spectrum processing, improved the intelligence level of EPR spectrum processing, and would help to enhance the efficiency of mass EPR spectra processing.
When performing eye movement pattern classification for different tasks, support vector machines are greatly affected by parameters. To address this problem, we propose an algorithm based on the improved whale algorithm to optimize support vector machines to enhance the performance of eye movement data classification. According to the characteristics of eye movement data, this study first extracts 57 features related to fixation and saccade, then uses the ReliefF algorithm for feature selection. To address the problems of low convergence accuracy and easy falling into local minima of the whale algorithm, we introduce inertia weights to balance local search and global search to accelerate the convergence speed of the algorithm and also use the differential variation strategy to increase individual diversity to jump out of local optimum. In this paper, experiments are conducted on eight test functions, and the results show that the improved whale algorithm has the best convergence accuracy and convergence speed. Finally, this paper applies the optimized support vector machine model of the improved whale algorithm to the task of classifying eye movement data in autism, and the experimental results on the public dataset show that the accuracy of the eye movement data classification of this paper is greatly improved compared with that of the traditional support vector machine method. Compared with the standard whale algorithm and other optimization algorithms, the optimized model proposed in this paper has higher recognition accuracy and provides a new idea and method for eye movement pattern recognition. In the future, eye movement data can be obtained by combining it with eye trackers to assist in medical diagnosis.
ObjectiveTo explore the correlation between quality of life and social support and anxiety level in children with epilepsy. MethodsA total of 207 children with epilepsy and their parents from March 2023 to December 2023 from Shanghai Children's Hospitalwere selected as the investigation objects, and the children's quality of life scale, Children's perceptive Social support Scale and PROMIS parental Report version anxiety brief form were used to investigate. The correlation between the quality of life of children with epilepsy and the level of social support and anxiety was analyzed. ResultsThe results of univariate analysis showed that the quality of life of children with epilepsy was affected by whether they had siblings and the frequency of onset in the past month (P<0.05). Pearson correlation analysis showed that social support was positively correlated with quality of life (P<0.05). The scores of anxiety and quality of life were negatively correlated (P<0.05). Social support was negatively correlated with anxiety scores (P<0.05). The results of multiple linear regression analysis showed that siblings, social support and anxiety were independent factors affecting the quality of life of children with epilepsy (P<0.05). ConclusionSocial support has a positive effect on the quality of life of children with epilepsy, anxiety level has a negative effect on the quality of life, and social support has a negative effect on anxiety. Therefore, clinical psychological support should be strengthened for children with epilepsy, fully mobilize their positive psychological factors, reduce their anxiety and other negative emotions, play a full range of social support, to achieve the goal of improving the quality of life.
Because of the diversity and complexity of clinical indicators, it is difficult to establish a comprehensive and reliable prediction model for induction of labor (IOL) outcomes with existing methods. This study aims to analyze the clinical indicators related to IOL and to develop and evaluate a prediction model based on a small-sample of data. The study population consisted of a total of 90 pregnant women who underwent IOL between February 2023 and January 2024 at the Shanghai First Maternity and Infant Healthcare Hospital, and a total of 52 clinical indicators were recorded. Maximal information coefficient (MIC) was used to select features for clinical indicators to reduce the risk of overfitting caused by high-dimensional features. Then, based on the features selected by MIC, the support vector machine (SVM) model based on small samples was compared and analyzed with the fully connected neural network (FCNN) model based on large samples in deep learning, and the receiver operating characteristic (ROC) curve was given. By calculating the MIC score, the final feature dimension was reduced from 55 to 15, and the area under curve (AUC) of the SVM model was improved from 0.872 before feature selection to 0.923. Model comparison results showed that SVM had better prediction performance than FCNN. This study demonstrates that SVM successfully predicted IOL outcomes, and the MIC feature selection effectively improves the model’s generalization ability, making the prediction results more stable. This study provides a reliable method for predicting the outcome of induced labor with potential clinical applications.
In order to improve the motion fluency and coordination of lower extremity exoskeleton robots and wearers, a pace recognition method of exoskeleton wearer is proposed base on inertial sensors. Firstly, the triaxial acceleration and triaxial angular velocity signals at the thigh and calf were collected by inertial sensors. Then the signal segment of 0.5 seconds before the current time was extracted by the time window method. And the Fourier transform coefficients in the frequency domain signal were used as eigenvalues. Then the support vector machine (SVM) and hidden Markov model (HMM) were combined as a classification model, which was trained and tested for pace recognition. Finally, the pace change rule and the human-machine interaction force were combined in this model and the current pace was predicted by the model. The experimental results showed that the pace intention of the lower extremity exoskeleton wearer could be effectively identified by the method proposed in this article. And the recognition rate of the seven pace patterns could reach 92.14%. It provides a new way for the smooth control of the exoskeleton.
Objective To explore the management practices of projects supported by National Natural Science Foundation in a large general hospital. Methods We carried out an overall analysis on the data of projects supported by National Natural Science Foundation of China from 2011 to 2016 in West China Hospital of Sichuan University. The characteristics of researchers who were granted the foundation were also studied. Results In the recent six years, there were 767 projects granted by the National Natural Science Foundation in the hospital, with an average of 127 projects a year. The Department of Medical Sciences was the top source of approval for the projects (690 items), and its granted projects covered all the categories set by the foundation committee. Most of the researchers who got the fund were between 25 and 40 years of age (501 projects). Researchers in charge of general projects were mainly professors with an age of (43.54±7.28) years old in average. Researchers in charge of Projects for Distinguished Young Scholars were mainly of medium-grade professional titles, and their average age was (32.01±3.05) years old. Moreover, among these young scholars, the age of male researchers [(31.27±2.23) years old] was significantly younger than that of female scholars [(32.90±3.62) years old](P<0.01). Conclusion It is more and more important for National Natural Science Foundation of China to study the accumulation of early research of young scholars and the leading role of the hospital in basic research, personnel training and discipline construction.
Open reduction and internal fixation with plate and screw is one of the most widely used surgical methods in the treatment of proximal humeral fractures in the elderly. In recent years, more and more studies have shown that it is very important to strengthen the medial column support of the proximal humerus during the surgery. At present, orthopedists often use bone graft, bone cement, medial support screw and medial support plate to strengthen the support of the medial column of the proximal humerus when applying open reduction and internal fixation with plate and screw to treat proximal humeral fractures. Therefore, the methods of strengthening medial column support for proximal humerus fractures and their effects on maintaining fracture reduction, reducing postoperative complications and improving functional activities of shoulder joints after operation are reviewed in this paper. It aims to provide a certain reference for the individualized selection of medial support methods according to the fracture situation in the treatment of proximal humeral fractures.