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.
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.
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.
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.
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.
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.
Objective To investigate the dietary patterns of rural residents in the high-incidence areas of esophageal cancer (EC), and to explore the clustering and influencing factors of risk factors associated with high-incidence characteristics. Methods A special structured questionnaire was applied to conduct a face-to-face survey on the dietary patterns of rural residents in Yanting county of Sichuan Province from July to August 2021. Univariate and multivariate logistic regression models were used to analyze the influencing factors of risk factor clustering for EC. Results There were 838 valid questionnaires in this study. A total of 90.8% of rural residents used clean water such as tap water. In the past one year, the people who ate fruits and vegetables, soybean products, onions and garlic in high frequency accounted for 69.5%, 32.8% and 74.5%, respectively; the people who ate kimchi, pickled vegetables, sauerkraut, barbecue, hot food and mildew food in low frequency accounted for 59.2%, 79.6%, 68.2%, 90.3%, 80.9% and 90.3%, respectively. The clustering of risk factors for EC was found in 73.3% of residents, and the aggregation of two risk factors was the most common mode (28.2%), among which tumor history and preserved food was the main clustering pattern (4.6%). The logistic regression model revealed that the gender, age, marital status and occupation were independent influencing factors for the risk factors clustering of EC (P<0.05). Conclusion A majority of rural residents in high-incidence areas of EC in Yanting county have good eating habits, but the clustering of some risk factors is still at a high level. Gender, age, marital status, and occupation are influencing factors of the risk factors clustering of EC.
In recent years, optical coherence tomography (OCT) has developed into a popular coronary imaging technology at home and abroad. The segmentation of plaque regions in coronary OCT images has great significance for vulnerable plaque recognition and research. In this paper, a new algorithm based on K-means clustering and improved random walk is proposed and Semi-automated segmentation of calcified plaque, fibrotic plaque and lipid pool was achieved. And the weight function of random walk is improved. The distance between the edges of pixels in the image and the seed points is added to the definition of the weight function. It increases the weak edge weights and prevent over-segmentation. Based on the above methods, the OCT images of 9 coronary atherosclerotic patients were selected for plaque segmentation. By contrasting the doctor’s manual segmentation results with this method, it was proved that this method had good robustness and accuracy. It is hoped that this method can be helpful for the clinical diagnosis of coronary heart disease.
Our team proposed and constructed an Expert-knowledge and Data-driven Comprehensive Evaluation Model of Chinese Patent Medicine (EDCEM-CPM) using the machine learning algorithm. This model could improve the system of the comprehensive evaluation of the Chinese patent medicine in technology and provide measurement tools for Chinese patent medicine according to its characteristics. The model evaluates the multi-dimensional value of Chinese patent medicine by data pre-treatment, clustering algorithms, and data training steps, such as automatic learning weighting. This evaluation model is already in practice. In this paper, we introduced the establishment of the model with the calculation process for reference.
