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.
The rapid development of high-throughput chromatin conformation capture (Hi-C) technology provides rich genomic interaction data between chromosomal loci for chromatin structure analysis. However, existing methods for identifying topologically associated domains (TADs) based on Hi-C data suffer from low accuracy and sensitivity to parameters. In this context, a TAD identification method based on spatial density clustering was designed and implemented in this paper. The method preprocessed the raw Hi-C data to obtain normalized Hi-C contact matrix data. Then, it computed the distance matrix between loci, generated a reachability graph based on the core distance and reachability distance of loci, and extracted clustering clusters. Finally, it extracted TAD boundaries based on clustering results. This method could identify TAD structures with higher coherence, and TAD boundaries were enriched with more ChIP-seq factors. Experimental results demonstrate that our method has advantages such as higher accuracy and practical significance in TAD identification.
Objective To classify the nursing needs of patients undergoing ophthalmic day surgery, to understand the characteristics and needs of different patient groups, and propose specific nursing strategies to further improve the nursing quality of the ophthalmic day wards. Methods A retrospective review was conducted on all archived electronic medical records of patients in the Ophthalmology Day Ward of Beijing Tongren Hospital affiliated to the Capital Medical University from January to September 2023. Statistical description and cluster analysis were used to analyze and cluster all data. Results A total of 52049 patients were included, with an average age of (57.11±19.61) years. The number of nursing items required was 0 for 3104 patients (5.96%), 1 for 9158 patients (17.59%), 2 for 25428 patients (48.85%), 3 for 8812 patients (16.93%), 4 for 5442 patients (10.46%), and 5-11 for 105 patients (0.20%). The number of patients’ comorbidities was 0 for 38653 patients (74.26%), 1 for 10896 patients (20.93%), 2 for 2449 patients (4.71%), and 3-11 for 51 patients (0.10%). Using the number of comorbidities, total required nursing care items, and age as clustering variables, the 52049 patients were divided into 3 groups: low nursing demand group with 11817 patients (22.70%), medium nursing demand group with 24466 patients (47.01%), and high nursing demand group with 15766 patients (30.29%). The results showed that both patient age and the number of comorbidities were closely related to the number of nursing care items needed. Conclusion Classifying and analyzing the nursing needs of patients undergoing ophthalmic day surgery can help understand the needs of different categories of patients, improve nursing strategies specifically, provide support for further improving the accuracy and quality of ophthalmic day care services, and provide reference for clinical nursing work.
Accurate segmentation of pulmonary nodules is an important basis for doctors to determine lung cancer. Aiming at the problem of incorrect segmentation of pulmonary nodules, especially the problem that it is difficult to separate adhesive pulmonary nodules connected with chest wall or blood vessels, an improved random walk method is proposed to segment difficult pulmonary nodules accurately in this paper. The innovation of this paper is to introduce geodesic distance to redefine the weights in random walk combining the coordinates of the nodes and seed points in the image with the space distance. The improved algorithm is used to achieve the accurate segmentation of pulmonary nodules. The computed tomography (CT) images of 17 patients with different types of pulmonary nodules were selected for segmentation experiments. The experimental results are compared with the traditional random walk method and those of several literatures. Experiments show that the proposed method has good accuracy in the segmentation of pulmonary nodule, and the accuracy can reach more than 88% with segmentation time is less than 4 seconds. The results could be used to assist doctors in the diagnosis of benign and malignant pulmonary nodules and improve clinical efficiency.
In clinic, intima and media thickness are the main indicators for evaluating the development of atherosclerosis. At present, these indicators are measured by professional doctors manually marking the boundaries of the inner and media on B-mode images, which is complicated, time-consuming and affected by many artificial factors. A grayscale threshold method based on Gaussian Mixture Model (GMM) clustering is therefore proposed to detect the intima and media thickness in carotid arteries from B-mode images in this paper. Firstly, the B-mode images are clustered based on the GMM, and the boundary between the intima and media of the vessel wall is then detected by the gray threshold method, and finally the thickness of the two is measured. Compared with the measurement technique using the gray threshold method directly, the clustering of B-mode images of carotid artery solves the problem of gray boundary blurring of inner and middle membrane, thereby improving the stability and detection accuracy of the gray threshold method. In the clinical trials of 120 healthy carotid arteries, means of 4 manual measurements obtained by two experts are used as reference values. Experimental results show that the normalized root mean square errors (NRMSEs) of the estimated intima and media thickness after GMM clustering were 0.104 7 ± 0.076 2 and 0.097 4 ± 0.068 3, respectively. Compared with the results of the direct gray threshold estimation, means of NRMSEs are reduced by 19.6% and 22.4%, respectively, which indicates that the proposed method has higher measurement accuracy. The standard deviations are reduced by 17.0% and 21.7%, respectively, which indicates that the proposed method has better stability. In summary, this method is helpful for early diagnosis and monitoring of vascular diseases, such as atherosclerosis.
Due to the minimum free energy model, it is very important to predict the RNA secondary structure accurately and efficiently from the suboptimal foldings. Using clustering techniques in analyzing the suboptimal structures could effectively improve the prediction accuracy. An improved k-medoids cluster method is proposed to make this a better accuracy with the RBP score and the incremental candidate set of medoids matrix in this paper. The algorithm optimizes initial medoids through an expanding medoids candidate sets gradually.The predicted results indicated this algorithm could get a higher value of CH and significantly shorten the time for calculating clustering RNA folding structures.
The diagnosis of pancreatic cancer is very important. The main method of diagnosis is based on pathological analysis of microscopic image of Pap smear slide. The accurate segmentation and classification of images are two important phases of the analysis. In this paper, we proposed a new automatic segmentation and classification method for microscopic images of pancreas. For the segmentation phase, firstly multi-features Mean-shift clustering algorithm (MFMS) was applied to localize regions of nuclei. Then, chain splitting model (CSM) containing flexible mathematical morphology and curvature scale space corner detection method was applied to split overlapped cells for better accuracy and robustness. For classification phase, 4 shape-based features and 138 textural features based on color spaces of cell nuclei were extracted. In order to achieve optimal feature set and classify different cells, chain-like agent genetic algorithm (CAGA) combined with support vector machine (SVM) was proposed. The proposed method was tested on 15 cytology images containing 461 cell nuclei. Experimental results showed that the proposed method could automatically segment and classify different types of microscopic images of pancreatic cell and had effective segmentation and classification results. The mean accuracy of segmentation is 93.46%±7.24%. The classification performance of normal and malignant cells can achieve 96.55%±0.99% for accuracy, 96.10%±3.08% for sensitivity and 96.80%±1.48% for specificity.
Magnetic resonance (MR) images can be used to detect lesions in the brains of patients with multiple sclerosis (MS). An automatic method is presented for segmentation of MS lesions using multispectral MR images in this paper. Firstly, a Pd-w image is subtracted from its corresponding T1-w images to get an image in which the cerebral spinal fluid (CSF) is enhanced. Secondly, based on kernel fuzzy c-means clustering (KFCM) algorithm, the enhanced image and the corresponding T2-w image are segmented respectively to extract the CSF region and the CSF-MS lesions combinatoin region. A raw MS lesions image is obtained by subtracting the CSF region from CSF-MS region. Thirdly, based on applying median filter and thresholding to the raw image, the MS lesions were detected finally. Results were tested on BrainWeb images and evaluated with Dice similarity coefficient (DSC), sensitivity (Sens), specificity (Spec) and accuracy (Acc). The testing results were satisfactory.
In order to develop safe training intensity and training methods for the passive balance rehabilitation training system, we propose in this paper a mathematical model for human standing balance adjustment based on T-S fuzzy identification method. This model takes the acceleration of a multidimensional motion platform as its inputs, and human joint angles as its outputs. We used the artificial bee colony optimization algorithm to improve fuzzy C-means clustering algorithm, which enhanced the efficiency of the identification for antecedent parameters. Through some experiments, the data of 9 testees were collected, which were used for model training and model results validation. With the mean square error and cross-correlation between the simulation data and measured data, we concluded that the model was accurate and reasonable.
At present, the incidence of Parkinson’s disease (PD) is gradually increasing. This seriously affects the quality of life of patients, and the burden of diagnosis and treatment is increasing. However, the disease is difficult to intervene in early stage as early monitoring means are limited. Aiming to find an effective biomarker of PD, this work extracted correlation between each pair of electroencephalogram (EEG) channels for each frequency band using weighted symbolic mutual information and k-means clustering. The results showed that State1 of Beta frequency band (P = 0.034) and State5 of Gamma frequency band (P = 0.010) could be used to differentiate health controls and off-medication Parkinson’s disease patients. These findings indicated that there were significant differences in the resting channel-wise correlation states between PD patients and healthy subjects. However, no significant differences were found between PD-on and PD-off patients, and between PD-on patients and healthy controls. This may provide a clinical diagnosis reference for Parkinson’s disease.