Data Masking for Chinese Electronic Medical Records with Named Entity Recognition

With the rapid development of information technology,the electronifi-cation of medical records has gradually become a trend.In China,the population base is huge and the supporting medical institutions are numerous,so this reality drives the conversion of paper medical records to electronic medical records.Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence,and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field.However,electronic medical records contain a large amount of private patient information,which must be desensitized before they are used as open resources.Therefore,to solve the above problems,data masking for Chinese electronic medical records with named entity recognition is proposed in this paper.Firstly,the text is vectorized to satisfy the required format of the model input.Secondly,since the input sentences may have a long or short length and the relationship between sentences in context is not negligible.To this end,a neural network model for named entity recognition based on bidirectional long short-term memory(BiLSTM)with conditional random fields(CRF)is constructed.Finally,the data masking operation is performed based on the named entity recog-nition results,mainly using regular expression filtering encryption and principal component analysis(PCA)word vector compression and replacement.In addi-tion,comparison experiments with the hidden markov model(HMM)model,LSTM-CRF model,and BiLSTM model are conducted in this paper.The experi-mental results show that the method used in this paper achieves 92.72%Accuracy,92.30%Recall,and 92.51%F1_score,which has higher accuracy compared with other models.

Dengue virus infection:A review of advances in the emerging rapid detection methods

Dengue virus infections are increasing worldwide generally and in Asia,Central and South America and Africa,particularly.It poses a serious threat to the children population.The rapid and accurate diagnostic systems are essentially required due to lack of effective vaccine against dengue virus and the progressive spread of the dengue virus infection.The recent progress in developing micro-and nano-fabrication techniques has led to low cost and scale down the biomedical point-of-care devices.Starting from the conventional and modern available methods for the diagnosis of dengue infection,this review examines several emerging rapid and point-of-care diagnostic devices that hold significant potential for the progress in smart diagnosis tools.The given review revealed that an effective vaccine is required urgently against all the dengue virus serotypes.However,the rapid detection methods of dengue virus help in early treatment and significantly reduce the dengue virus outbreak.

Comparative Study of Cotton Plant Height Difference in the Arid Areas Based on LandSat8 OLI Data

With the cotton field of Regiment 105,Division 6 of Production and Construction Corps of Xinjiang as the object of study,this paper uses remote sensing,geographic information system technology and statistical analysis software to analyze the correlation between 18 sets of spectral data and cotton plant height,with a view to exploring the characteristics of difference in image spectrum parameters and related indices under different cotton plant height. The results show that the correlation between spectral reflectance and plant height reaches a highly significant level in blue light,green light,red light and near infrared bands,and NDVI and LAI vegetation indices established using red light and near infrared bands are significantly and positively correlated with plant height.

On-chip classification of micro-particles using laser light scattering and machine learning

The rapid detection of microparticles exhibits a broad range of applications in the field of science and technology. The proposed method differentiates and identifies the 2 μm and 5 μm sized particles using a laser light scattering. The detection method is based on measuring forward light scattering from the particles and then classifying the acquired data using support vector machines. The device is composed of a microfluidic chip linked with photosensors and a laser device using optical fiber. Connecting the photosensors and laser device using optical fibers makes the device more diminutive in size and portable. The prepared sample containing microspheres was passed through the channel, and the surrounding photosensors measured the scattered light. The time-domain features were evaluated from the acquired scattered light, and then the SVM classifier was trained to distinguish the particle’s data. The real-time detection of the particles was performed with an overall classification accuracy of 96.06%. The optimum conditions were evaluated to detect the particles with a minimum concentration of 0.2 μg/m L. The developed system is anticipated to be helpful in developing rapid testing devices for detecting pathogens ranging between 2 μm to 10 μm.