In clinical, manually scoring by technician is the major method for sleep arousal detection. This method is time-consuming and subjective. This study aimed to achieve an end-to-end sleep-arousal events detection by constructing a convolutional neural network based on multi-scale convolutional layers and self-attention mechanism, and using 1 min single-channel electroencephalogram (EEG) signals as its input. Compared with the performance of the baseline model, the results of the proposed method showed that the mean area under the precision-recall curve and area under the receiver operating characteristic were both improved by 7%. Furthermore, we also compared the effects of single modality and multi-modality on the performance of the proposed model. The results revealed the power of single-channel EEG signals in automatic sleep arousal detection. However, the simple combination of multi-modality signals may be counterproductive to the improvement of model performance. Finally, we also explored the scalability of the proposed model and transferred the model into the automated sleep staging task in the same dataset. The average accuracy of 73% also suggested the power of the proposed method in task transferring. This study provides a potential solution for the development of portable sleep monitoring and paves a way for the automatic sleep data analysis using the transfer learning method.
Sleep ; Sleep Stages ; Arousal ; Data Analysis ; Electroencephalography

OBJECTIVE: To compare the predictive ability of two extended Cox models in nonlinear survival data analysis. METHODS: Through Monte Carlo simulation and empirical study and with the conventional Cox Proportional Hazards model and Random Survival Forests as the reference models, we compared restricted cubic spline Cox model (Cox_RCS) and DeepSurv neural network Cox model (Cox_DNN) for their prediction ability in nonlinear survival data analysis. Concordance index was used to evaluate the differentiation of the prediction results (a larger concordance index indicates a better prediction ability of the model). Integrated Brier Score was used to evaluate the calibration degree of the prediction (a smaller index indicates a better prediction ability). RESULTS: For data that met requirement of the proportion risk, the Cox_RCS model had the best prediction ability regardless of the sample size or deletion rate. For data that failed to meet the proportion risk, the prediction ability of Cox_DNN was optimal for a large sample size (≥500) with a low deletion (< 40%); the prediction ability of Cox_RCS was superior to those of other models in all other scenarios. For example data, the Cox_RCS model showed the best performance. CONCLUSION In analysis of nonlinear low maintenance data, Cox_RCS and Cox_DNN have their respective advantages and disadvantages in prediction. The conventional survival analysis methods are not inferior to machine learning or deep learning methods under certain conditions.
Proportional Hazards Models ; Survival Analysis ; Calibration ; Computer Simulation ; Data Analysis

OBJECTIVE: To assess the influence of FLT3 expression on the prognosis of patients with acute myeloid leukemia (AML) by cell experiment and clinical data analysis. METHODS: Models for FLT3 over-expression and interference-expression in AML cells were constructed. The level of BAK gene expression and its protein product was determined, along with the proliferation and apoptosis of leukemia cells. FLT3 gene expression and FLT3-ITD variant were determined among patients with newly diagnosed AML. RESULTS: Compared with the interference-expression group, the level of BAK gene expression and its protein in FLT3 over-expression AML cells was significantly lower (P < 0.001), which also showed significantly faster proliferation (P < 0.001) and lower rate of apoptosis (P < 0.001). The expression level of FLT3 gene among patients with newly diagnosed AML was also significantly higher compared with the healthy controls (P < 0.001). The FLT3 gene expression of FLT3-ITD positive AML patients was higher than that of FLT3-WT patients (P = 0.002). Survival analysis showed that AML patients with high FLT3 expression in the medium-risk group had a lower complete remission rate and overall survival rate compared with those with a low FLT3 expression (P < 0.001). CONCLUSION Over-expression of FLT3 may influence the course of AML by promoting the proliferation of leukemia cells and inhibiting their apoptosis, which in turn may affect the prognosis of patients and serve as a negative prognostic factor for AML.
Humans ; Apoptosis/genetics* ; Data Analysis ; Leukemia, Myeloid, Acute/genetics* ; Gene Expression ; fms-Like Tyrosine Kinase 3/genetics*

Ventilator is an important medical instrument which can replace the function of autonomous ventilation artificially. Its safety and reliability are related to the health and even life safety of patients. With the publishing of the new national standard and international standard for ventilators, higher requirements are put forward for the detection and evaluation. This study mainly introduces an automatic test system for ventilator performance. The test system is based on PF-300 air-flow analyzer of Imtmedical and standard simulation lung. The automatic switch module of simulation lung is developed, and the automatic test system of ventilator is designed using the software development platform based on Python. It can not only automatically test all ventilation control parameters and monitoring parameters of the ventilator, but also realize automatic data recording, form reports and data analysis, and improve the efficiency and quality of inspection, detection and quality control.
Humans ; Reproducibility of Results ; Ventilators, Mechanical ; Computer Simulation ; Data Analysis ; Quality Control

Mining data from traditional Chinese medicine(TCM) prescriptions is one of the important methods for inheriting the experience of famous doctors and developing new drugs. However, current research work has problems such as to be optimized research plans and non-standard statistics. The main problems and corresponding solutions summarized by the research mainly include four aspects.(1)The research plan design needs to consider the efficacy and quality of individual cases.(2)The significance of the difference in confidence order of association rules needs to be further considered, and the lift should not be ignored.(3)The clustering analysis steps are complex. The selection of clustering variables should comprehensively consider factors such as the frequency of TCM, network topology parameters, and practical application significance. The selection of distance calculation and clustering methods should be improved based on the characteristics of TCM clinical data. Jaccard distance and its improvement plan should be given attention in the future. A single, unexplained clustering result should not be presented, but the final clustering plan should be selected based on a comprehensive consideration of TCM clinical characteristics and objective evaluation indicators for clustering.(4)When calculating correlation coefficients, algorithms that are only suitable for continuous variables should not be applied to binary variables. This article explained the connotations of the above problems based on the characteristics of TCM clinical research and statistical principles and proposed corresponding suggestions to provide important references for future data mining research work.
Humans ; Medicine, Chinese Traditional ; Prescriptions ; Data Mining ; Cluster Analysis ; Physicians ; Drugs, Chinese Herbal/therapeutic use*

Chromatin three-dimensional genome structure plays a key role in cell function and gene regulation. Single-cell Hi-C techniques can capture genomic structure information at the cellular level, which provides an opportunity to study changes in genomic structure between different cell types. Recently, some excellent computational methods have been developed for single-cell Hi-C data analysis. In this paper, the available methods for single-cell Hi-C data analysis were first reviewed, including preprocessing of single-cell Hi-C data, multi-scale structure recognition based on single-cell Hi-C data, bulk-like Hi-C contact matrix generation based on single-cell Hi-C data sets, pseudo-time series analysis, and cell classification. Then the application of single-cell Hi-C data in cell differentiation and structural variation was described. Finally, the future development direction of single-cell Hi-C data analysis was also prospected.
Chromatin ; Genome ; Single-Cell Analysis/methods* ; Cell Differentiation ; Data Analysis

Acquired Immunodeficiency Syndrome/epidemiology* ; Bayes Theorem ; COVID-19/prevention & control* ; China/epidemiology* ; Communicable Disease Control/trends* ; Disease Notification/statistics & numerical data* ; Forecasting ; Humans ; Incidence ; Interrupted Time Series Analysis ; SARS-CoV-2

OBJECTIVE: To investigate the pathogenesis and clinical diagnosis of fetal/neonatal alloimmune thrombocytopenia (FNAIT) and analyze the laboratory test results and clinical data related to the disease, so as to provide reference for clinical treatment and improvement of prognosis. METHODS: The clinical data of six neonatal patients with FNAIT in the Neonatology Department of our hospital from March 2017 to September 2020 were retrospectively analyzed, which included laboratory diagnosis, clinical symptoms, treatment, and prognosis. RESULTS: Among six patients, two cases occurred in the first pregnancy and four cases in the second pregnancy. The platelet count of six cases were decreased at admission or during hospitalization and maternal and neonatal serum autoimmune platelet antibody tests were positive. Five cases were accompanied by different degrees of skin and facial bleeding spots or petechiae and ecchymosis, intracranial hemorrhage. Four cases were treated with immunoglobulin and/or steroid hormone therapy (one of them received cross-matched platelets transfusion), while the symptoms of the other two cases improved spontaneously. Five cases recovered and were discharged from the hospital, while one case had not recovered but the family members requested to be discharged forwardly. Four cases were hospitalized within two weeks, but two cases were hospitalized for more than two weeks due to other diseases or factors (e.g., neonatal sepsis, neonatal enteritis, congenital heart disease, neonatal asphyxia, etc.). CONCLUSION FNAIT is characterized by decreased platelet count, with or without bleeding symptoms, and may occur in the first and following pregnancy. FNAIT can recover spontaneously or have a good prognosis after treatment. However, the complication with other diseases or factors may affect the prognosis.
Adult ; Aged ; Antigens, Human Platelet ; Data Analysis ; Female ; Hemorrhage ; Humans ; Infant, Newborn ; Middle Aged ; Platelet Count ; Platelet Transfusion/adverse effects* ; Pregnancy ; Retrospective Studies ; Thrombocytopenia, Neonatal Alloimmune/therapy*

Objectives: Hand, foot and mouth disease (HFMD) is a widespread infectious disease that causes a significant disease burden on society. To achieve early intervention and to prevent outbreaks of disease, we propose a novel warning model that can accurately predict the incidence of HFMD. Methods: We propose a spatial-temporal graph convolutional network (STGCN) that combines spatial factors for surrounding cities with historical incidence over a certain time period to predict the future occurrence of HFMD in Guangdong and Shandong between 2011 and 2019. The 2011-2018 data served as the training and verification set, while data from 2019 served as the prediction set. Six important parameters were selected and verified in this model and the deviation was displayed by the root mean square error and the mean absolute error. Results: As the first application using a STGCN for disease forecasting, we succeeded in accurately predicting the incidence of HFMD over a 12-week period at the prefecture level, especially for cities of significant concern. Conclusions This model provides a novel approach for infectious disease prediction and may help health administrative departments implement effective control measures up to 3 months in advance, which may significantly reduce the morbidity associated with HFMD in the future.
China/epidemiology* ; Cities/epidemiology* ; Data Visualization ; Disease Outbreaks/statistics & numerical data* ; Forecasting/methods* ; Hand, Foot and Mouth Disease/prevention & control* ; Humans ; Incidence ; Neural Networks, Computer ; Reproducibility of Results ; Spatio-Temporal Analysis ; Time Factors

OBJECTIVES: To reduce the dimension of characteristic information extracted from pelvic CT images by using principal component analysis (PCA) and partial least squares (PLS) methods. To establish a support vector machine (SVM) classification and identification model to identify if there is pelvic injury by the reduced dimension data and evaluate the feasibility of its application. METHODS: Eighty percent of 146 normal and injured pelvic CT images were randomly selected as training set for model fitting, and the remaining 20% was used as testing set to verify the accuracy of the test, respectively. Through CT image input, preprocessing, feature extraction, feature information dimension reduction, feature selection, parameter selection, model establishment and model comparison, a discriminative model of pelvic injury was established. RESULTS: The PLS dimension reduction method was better than the PCA method and the SVM model was better than the naive Bayesian classifier (NBC) model. The accuracy of the modeling set, leave-one-out cross validation and testing set of the SVM classification model based on 12 PLS factors was 100%, 100% and 93.33%, respectively. CONCLUSIONS In the evaluation of pelvic injury, the pelvic injury data mining model based on CT images reaches high accuracy, which lays a foundation for automatic and rapid identification of pelvic injuries.
Algorithms ; Bayes Theorem ; Data Mining ; Least-Squares Analysis ; Support Vector Machine