Optimization of ethanol extraction process for Yihuang powder by orthogonal experiment combined with AHP and GA-BP neural network / 中国药房

OBJECTIVE To optimize ethanol extraction process of Yihuang powder. METHODS An orthogonal experiment was designed by reflux extraction with ethanol volume fraction， liquid-to-material ratio， and extraction time as investigation factors. The parameters used were the contents of hesperidin， nobiletin， tangeretin， gallic acid， chebulagic acid， chebulinic acid， liquiritin， glycyrrhizin， eugenol， and the paste-forming rate. The analytic hierarchy process （AHP） was used to calculate the comprehensive score. The optimal ethanol extraction process parameters of Yihuang powder were determined by verifying the results predicted by orthogonal experiment and genetic algorithm （GA）-back propagation neural network （BP neural network）. RESULTS The optimal ethanol extraction process parameters， as optimized by orthogonal experiment， were as follows: ethanol volume fraction of 60%， liquid-solid ratio of 14∶1 （mL/g)， extraction time of 90 min， and extraction for 2 times. The comprehensive score obtained by verification was 79.19. Meanwhile， the optimal ethanol extraction process parameters， optimized by GA-BP neural network， were ethanol volume fraction of 65%， liquid-solid ratio of 14∶1 （mL/g )， extraction time of 60 min， and extraction for 2 times. The comprehensive score obtained by verification was 85.30， higher than the results obtained from orthogonal experiment. CONCLUSIONS The optimization method of orthogonal experiment combined with GA-BP neural network is superior to the traditional orthogonal experiment optimization method. The optimized ethanol extraction process of Yihuang powder is stable and reliable.

Optimization of the pressurized processing technology of Strychnos nux-vomica boiled with mung beans based on genetic algorithm / 中国药房

OBJECTIVE To optimize the pressurized processing technology of Strychnos nux-vomica boiled with mung beans. METHODS The least squares method was used to establish a one-dimensional model for the effects of four factors， namely， processing time， processing pressure， mung bean dosage and water added， on the contents of strychnine and toxiferine， and the multivariate model hypothesis was proposed by analyzing the function of one-dimensional model. Based on the orthogonal experiment， the genetic algorithm was used to solve the undetermined coefficients in the model. A bi-objective optimization model based on strychnine and toxiferine content was constructed according to the actual conditions， and the optimal technology was obtained by solving the model function and validated. RESULTS The optimal processing technology was boiling S. nux-vomica with mung beans at 2.393 MPa saturated steam pressure for 5.5 h， and then draining； rinsing to remove mung beans， scraping off the bark of S. nux-vomica and cutting into slice of 0.6 mm； using 180 g of mung beans and 15 L of water per 500 g of S. nux- vomica. CONCLUSIONS The optimized pressurized processing technology is stable and feasible， and can provide a reference for the optimization of processing technology of S. nux-vomica boiled with mung beans.

Accurate Reconstruction of Traffic Accident Based on Multiple Optimization Algorithms and Evaluation of Craniocerebral Injury Risk / 医用生物力学

Objective To investigate the effect of different optimization algorithms on accurate reconstruction of traffic accidents. Methods Non-dominated sorting genetic algorithm-II ( NSGA-II), neighborhood cultivation genetic algorithm (NCGA) and multi-objective particle swarm optimization (MOPSO) were used to optimize the multi-rigid body dynamic reconstruction of a real case. The effects of different optimization algorithms on convergence speed and optimal approximate solution were studied. The optimal initial impact parameters were simulated as boundary conditions of finite element method, and the simulated results were compared with the actual injuries. Results NCGA had a faster convergence speed and a better result in optimization process. The kinematic response of pedestrian vehicle collision reconstructed by the optimal approximate solution was consistent with the surveillance video. The prediction of craniocerebral injury was basically consistent with the cadaver examination. Conclusions The combination of optimization algorithm, rigid multibody and finite element method can complete the accurate reconstruction of traffic accidents and reduce the influence of human factors.

COVID-19 Outcome Prediction by Integrating Clinical and Metabolic Data using Machine Learning Algorithms

ABSTRACT Background: The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and is responsible for nearly 6 million deaths worldwide in the past 2 years. Machine learning (ML) models could help physicians in identifying high-risk individuals. Objectives: To study the use of ML models for COVID-19 prediction outcomes using clinical data and a combination of clinical and metabolic data, measured in a metabolomics facility from a public university. Methods: A total of 154 patients were included in the study. "Basic profile" was considered with clinical and demographic variables (33 variables), whereas in the "extended profile," metabolomic and immunological variables were also considered (156 characteristics). A selection of features was carried out for each of the profiles with a genetic algorithm (GA) and random forest models were trained and tested to predict each of the stages of COVID-19. Results: The model based on extended profile was more useful in early stages of the disease. Models based on clinical data were preferred for predicting severe and critical illness and death. ML detected trimethylamine N-oxide, lipid mediators, and neutrophil/lymphocyte ratio as important variables. Conclusion: ML and GAs provided adequate models to predict COVID-19 outcomes in patients with different severity grades.

Parameter Identification Algorithm of Lumped Parameter Model in the Circle of Willis / 医用生物力学

Objective To explore the application of three parameter identification methods (impedance modulus curve method, impedance component method, and genetic algorithm) in solving parameter identification problem of the 11-element lumped parameter model in the circle of Willis. Methods Using the flow and pressure waveforms of the internal carotid arteries and vertebral arteries on both sides as inlet conditions, parameter values of the model under normal and bilateral vertebral artery stenosis conditions were calculated. The recognition algorithm was verified by using Simulink models, and finally the stability of the recognition algorithm was verified by adding a certain noise to the flow. Results Under normal circumstances, the proximal resistances obtained by the impedance modulus curve method were larger, and the resistances of the anterior communicating artery obtained by the impedance component method were larger. The genetic algorithm could obtain relatively reasonable model parameter values. In the case of vertebral artery stenosis on both sides, the impedance modulus curve method could obviously get the results of the increasement in proximal resistances of the posterior circulation, but the results obtained by the impedance component method and the genetic algorithm mainly lied in that the distal resistance had a larger increase. Conclusions There are still differences between the pressure data calculated by the parameters identified by the above three methods and the actual data, which are considered as modeling errors, source data errors and calculation errors. The impedance modulus curve method has a certain effect in distinguishing changes of the proximal and distal resistances, but there exist large errors in identification of some parameters. The impedance component method can identify the parameters, but this method is unstable with large calculation errors. Genetic algorithm can obtain a better approximate solution, but it has certain problems in distinguishing vertebral artery stenosis. The combination of impedance modulus curve method and genetic algorithm may play a better role in future application of this model for disease diagnosis.

Application of near infrared spectroscopy to predict contents of various lactones in chromatographic process of Ginkgo Folium / 中国中药杂志

This study established a method for rapid quantification of terpene lactone, bilobalide, ginkgolide C, ginkgolide A and ginkgolide B in the chromatographic process of Ginkgo Folium based on near infrared spectroscopy(NIRS). The effects of competitive adaptive reweighting sampling(CARS), random frog(RF), and synergy interval partial least squares(siPLS) on the performance of partial least squares regression(PLSR) model were compared to the reference values measured by HPLC. Among them, the correlation coefficients of prediction(Rp) of validation sets of terpene lactone, bilobalide, and ginkgolide C were all higher than 0.98, and the relative standard errors of prediction(RSEPs) were 5.87%, 6.90% and 6.63%, respectively. Aiming at ginkgolide A and ginkgolide B with relatively low content, the genetic algorithm joint extreme learning machine(GA-ELM) was used to establish the optimized quantitative analysis model. Compared with CARS-PLSR model, the CARS-GA-ELM models of ginkgolide A and ginkgolide B exhibited a reduction in RSEP from 15.65% to 8.52% and from 21.28% to 10.84%, respectively, which met the needs of quantitative ana-lysis. It has been proved that NIRS can be used for the rapid detection of various lactone components in the chromatographic process of Ginkgo Folium.

Design and simulation of dynamic hip prosthesis based on remote motion center mechanism / 生物医学工程学杂志

The rotation center of traditional hip disarticulation prosthesis is often placed in the front and lower part of the socket, which is asymmetric with the rotation center of the healthy hip joint, resulting in poor symmetry between the prosthesis movement and the healthy lower limb movement. Besides, most of the prosthesis are passive joints, which need to rely on the amputee's compensatory hip lifting movement to realize the prosthesis movement, and the same walking movement needs to consume 2-3 times of energy compared with normal people. This paper presents a dynamic hip disarticulation prosthesis (HDPs) based on remote center of mechanism (RCM). Using the double parallelogram design method, taking the minimum size of the mechanism as the objective, the genetic algorithm was used to optimize the size, and the rotation center of the prosthesis was symmetrical with the rotation center of the healthy lower limb. By analyzing the relationship between the torque and angle of hip joint in the process of human walking, the control system mirrored the motion parameters of the lower on the healthy side, and used the parallel drive system to provide assistance for the prosthesis. Based on the established virtual prototype simulation platform of solid works and Adams, the motion simulation of hip disarticulation prosthesis was carried out and the change curve was obtained. Through quantitative comparison with healthy lower limb and traditional prosthesis, the scientificity of the design scheme was analyzed. The results show that the design can achieve the desired effect, and the design scheme is feasible.

A fetal electrocardiogram signal extraction method based on long short term memory network optimized by genetic algorithm / 生物医学工程学杂志

Fetal electrocardiogram signal extraction is of great significance for perinatal fetal monitoring. In order to improve the prediction accuracy of fetal electrocardiogram signal, this paper proposes a fetal electrocardiogram signal extraction method (GA-LSTM) based on genetic algorithm (GA) optimization with long and short term memory (LSTM) network. Firstly, according to the characteristics of the mixed electrocardiogram signal of the maternal abdominal wall, the global search ability of the GA is used to optimize the number of hidden layer neurons, learning rate and training times of the LSTM network, and the optimal combination of parameters is calculated to make the network topology and the mother body match the characteristics of the mixed signals of the abdominal wall. Then, the LSTM network model is constructed using the optimal network parameters obtained by the GA, and the nonlinear transformation of the maternal chest electrocardiogram signals to the abdominal wall is estimated by the GA-LSTM network. Finally, using the non-linear transformation obtained from the maternal chest electrocardiogram signal and the GA-LSTM network model, the maternal electrocardiogram signal contained in the abdominal wall signal is estimated, and the estimated maternal electrocardiogram signal is subtracted from the mixed abdominal wall signal to obtain a pure fetal electrocardiogram signal. This article uses clinical electrocardiogram signals from two databases for experimental analysis. The final results show that compared with the traditional normalized minimum mean square error (NLMS), genetic algorithm-support vector machine method (GA-SVM) and LSTM network methods, the method proposed in this paper can extract a clearer fetal electrocardiogram signal, and its accuracy, sensitivity, accuracy and overall probability have been better improved. Therefore, the method could extract relatively pure fetal electrocardiogram signals, which has certain application value for perinatal fetal health monitoring.

Detection of inferior myocardial infarction based on morphological characteristics / 生物医学工程学杂志

Early accurate detection of inferior myocardial infarction is an important way to reduce the mortality from inferior myocardial infarction. Regrading the existing problems in the detection of inferior myocardial infarction, complex model structures and redundant features, this paper proposed a novel inferior myocardial infarction detection algorithm. Firstly, based on the clinic pathological information, the peak and area features of QRS and ST-T wavebands as well as the slope feature of ST waveband were extracted from electrocardiogram (ECG) signals leads Ⅱ, Ⅲ and aVF. In addition, according to individual features and the dispersion between them, we applied genetic algorithm to make judgement and then input the feature with larger degree into support vector machine (SVM) to realize the accurate detection of inferior myocardial infarction. The proposed method in this paper was verified by Physikalisch-Technische Bundesanstalt (PTB) diagnostic electrocardio signal database and the accuracy rate was up to 98.33%. Conforming to the clinical diagnosis and the characteristics of specific changes in inferior myocardial infarction ECG signal, the proposed method can effectively make precise detection of inferior myocardial infarction by morphological features, and therefore is suitable to be applied in portable devices development for clinical promotion.

Early diagnosis of Alzheimer's disease based on three-dimensional convolutional neural networks ensemble model combined with genetic algorithm / 生物医学工程学杂志

The pathogenesis of Alzheimer's disease (AD), a common neurodegenerative disease, is still unknown. It is difficult to determine the atrophy areas, especially for patients with mild cognitive impairment (MCI) at different stages of AD, which results in a low diagnostic rate. Therefore, an early diagnosis model of AD based on 3-dimensional convolutional neural network (3DCNN) and genetic algorithm (GA) was proposed. Firstly, the 3DCNN was used to train a base classifier for each region of interest (ROI). And then, the optimal combination of the base classifiers was determined with the GA. Finally, the ensemble consisting of the chosen base classifiers was employed to make a diagnosis for a patient and the brain regions with significant classification capability were decided. The experimental results showed that the classification accuracy was 88.6% for AD

Rapid determination of active components in Ginkgo biloba leaves by near infrared spectroscopy combined with genetic algorithm joint extreme learning machine / 中国中药杂志

Near-infrared spectroscopy(NIRS) combined with band screening method and modeling algorithm can be used to achieve the rapid and non-destructive detection of the traditional Chinese medicine(TCM) production process. This paper focused on the ginkgo leaf macroporous resin purification process, which is the key technology of Yinshen Tongluo Capsules, in order to achieve the rapid determination of quercetin, kaempferol and isorhamnetin in effluent. The abnormal spectrum was eliminated by Mahalanobis distance algorithm, and the data set was divided by the sample set partitioning method based on joint X-Y distances(SPXY). The key information bands were selected by synergy interval partial least squares(siPLS); based on that, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA) and Monte Carlo uninformative variable(MC-UVE) were used to select wavelengths to obtain less but more critical variable data. With selected key variables as input, the quantitative analysis model was established by genetic algorithm joint extreme learning machine(GA-ELM) algorithm. The performance of the model was compared with that of partial least squares regression(PLSR). The results showed that the combination with siPLS-CARS-GA-ELM could achieve the optimal model performance with the minimum number of variables. The calibration set correlation coefficient R_c and the validation set correlation coefficient R_p of quercetin, kaempferol and isorhamnetin were all above 0.98. The root mean square error of calibration(RMSEC), the root mean square error of prediction(RMSEP) and the relative standard errors of prediction(RSEP) were 0.030 0, 0.029 2 and 8.88%, 0.041 4, 0.034 8 and 8.46%, 0.029 3, 0.027 1 and 10.10%, respectively. Compared with the PLSR me-thod, the performance of the GA-ELM model was greatly improved, which proved that NIRS combined with GA-ELM method has a great potential for rapid determination of effective components of TCM.

Multi-index optimization of extraction process of Fengyin Decoction based on BAS-GA-BP neural network combined with entropy weight method / 中国中药杂志

To optimize the ethanol extraction technology parameters of Fengyin Decoction by orthogonal experiment combined with beetle antennae search(BAS)-genetic algorithm(GA)-back propagation neural network(BPNN). Based on single factor investigation, the extraction temperature, ethanol volume, extraction time, and ethanol concentration were used as orthogonal experiment factors, and entropy weight method was used to calculate the comprehensive scores of aloe-emodin, glycyrrhizic acid ammonium salt, rhein, emodin, chrysophanol, physcion, cinnamaldehyde, 6-gingerol, extraction ratio and fingerprint similarity. BAS-BPNN model was established, and then, GA was used to predict the optimal extraction process. The results showed that BAS-BPNN was optimized to obtain the optimal ethanol extraction process of Fengyin Decoction as follows: extraction temperature of 87 ℃, adding 9 times of 75 % ethanol, and extracting for 47 minutes, with a comprehensive score of 1.052 9. Meanwhile, the optimal process parameters obtained by orthogonal design were as follows: the extraction temperature of 80 ℃, adding 10 times of 75% ethanol, extracting for 30 minutes, with a comprehensive score of 1.003 7. The comprehensive score of the process obtained from the BAS-BPNN model was slightly better than that from the orthogonal test, indicating that the optimized process from BAS-BPNN model was more ideal, so it was finally determined as the best extraction process for Fengyin Decoction. The process of Fengyin Decoction obtained from BAS-GA-BPNN has high extraction efficiency and good stability, which provides reference for the subsequent development and quality control.

Epidemiological characteristics of bacillary dysentery from 2009 to 2016 and its incidence prediction model based on meteorological factors

BACKGROUND@#This study aimed to analyse the epidemiological characteristics of bacillary dysentery (BD) caused by Shigella in Chongqing, China, and to establish incidence prediction models based on the correlation between meteorological factors and BD, thus providing a scientific basis for the prevention and control of BD.@*METHODS@#In this study, descriptive methods were employed to investigate the epidemiological distribution of BD. The Boruta algorithm was used to estimate the correlation between meteorological factors and BD incidence. The genetic algorithm (GA) combined with support vector regression (SVR) was used to establish the prediction models for BD incidence.@*RESULTS@#In total, 68,855 cases of BD were included. The incidence declined from 36.312/100,000 to 23.613/100,000, with an obvious seasonal peak from May to October. Males were more predisposed to the infection than females (the ratio was 1.118:1). Children < 5 years old comprised the highest incidence (295.892/100,000) among all age categories, and pre-education children comprised the highest proportion (34,658 cases, 50.335%) among all occupational categories. Eight important meteorological factors, including the highest temperature, average temperature, average air pressure, precipitation and sunshine, were correlated with the monthly incidence of BD. The obtained mean absolute percent error (MAPE), mean squared error (MSE) and squared correlation coefficient (R) of GA_SVR_MONTH values were 0.087, 0.101 and 0.922, respectively.@*CONCLUSION@#From 2009 to 2016, BD incidence in Chongqing was still high, especially in the main urban areas and among the male and pre-education children populations. Eight meteorological factors, including temperature, air pressure, precipitation and sunshine, were the most important correlative feature sets of BD incidence. Moreover, BD incidence prediction models based on meteorological factors had better prediction accuracies. The findings in this study could provide a panorama of BD in Chongqing and offer a useful approach for predicting the incidence of infectious disease. Furthermore, this information could be used to improve current interventions and public health planning.

Analysis of epileptic seizure detection method based on improved genetic algorithm optimization back propagation neural network / 生物医学工程学杂志

In order to improve the accuracy and efficiency of automatic seizure detection, the paper proposes a method based on improved genetic algorithm optimization back propagation (IGA-BP) neural network for epilepsy diagnosis, and uses the method to achieve detection of clinical epilepsy rapidly and effectively. Firstly, the method extracted the linear and nonlinear features of the epileptic electroencephalogram (EEG) signals and used a Gaussian mixture model (GMM) to perform cluster analysis on EEG features. Next, expectation maximization (EM) algorithm was used to estimate GMM parameters to calculate the optimal parameters for the selection operator of genetic algorithm (GA). The initial weights and thresholds of the BP neural network were obtained through using the improved genetic algorithm. Finally, the optimized BP neural network is used for the classification of the epileptic EEG signals to detect the epileptic seizure automatically. Compared with the traditional genetic algorithm optimization back propagation (GA-BP), the IGA-BP neural network can improve the population convergence rate and reduce the classification error. In the process of automatic detection of epilepsy, the method improves the detection accuracy in the automatic detection of epilepsy disorders and reduced inspection time. It has important application value in the clinical diagnosis and treatment of epilepsy.

Intelligent fetal state assessment based on genetic algorithm and least square support vector machine / 生物医学工程学杂志

Cardiotocography (CTG) is a commonly used technique of electronic fetal monitoring (EFM) for evaluating fetal well-being, which has the disadvantage of lower diagnostic rate caused by subjective factors. To reduce the rate of misdiagnosis and assist obstetricians in making accurate medical decisions, this paper proposed an intelligent assessment approach for analyzing fetal state based on fetal heart rate (FHR) signals. First, the FHR signals from the public database of the Czech Technical University-University Hospital in Brno (CTU-UHB) was preprocessed, and the comprehensive features were extracted. Then the optimal feature subset based on the -nearest neighbor (KNN) genetic algorithm (GA) was selected. At last the classification using least square support vector machine (LS-SVM) was executed. The experimental results showed that the classification of fetal state achieved better performance using the proposed method in this paper: the accuracy is 91%, sensitivity is 89%, specificity is 94%, quality index is 92%, and area under the receiver operating characteristic curve is 92%, which can assist clinicians in assessing fetal state effectively.

A Genetic Algorithm Approach for Semi-Supervised Clustering Algorithm

Introduction:A semi-supervised clustering algorithm is proposed that combines the benefits of supervised and unsupervised learningmethods. The approach allows unlabeled data with no known class to be used to improve classification accuracy [2]. The objectivefunction of an unsupervised technique, e.g. K-means clustering, is modified to minimize both the cluster dispersion of the inputattributes and a measure of cluster impurity based on the class labels. Minimizing the cluster dispersion of the examples is a form ofcapacity control to prevent over fitting [4]. For the output labels, impurity measures from decision tree algorithms such as the Gini indexcan be used. A genetic algorithm optimizes the objective function to produce clusters. Experimental results show that using classinformation improves the generalization ability compared to unsupervised methods based only on the input attributes [6]. Trainingusing information from unlabeled data can improve classification accuracy on that data as well. Genetic Algorithms (GAs) have beenwidely used in optimization problems for their high ability in seeking better and acceptable solutions within limited time. Clusteringensemble has emerged as another flavour of optimal solutions for generating more stable and robust partition from existing clusters [1].GAs has proved a major contribution to find consensus cluster partitions during clustering ensemble. Currently, web videocategorization has been an ever challenging research area with the popularity of the social web. In this paper, we propose a framework forweb video categorization using their textual features, video relations and web support [3]. There are three contributions in this researchwork. First, we expand the traditional Vector Space Model (VSM) in a more generic manner as Semantic VSM (S-VSM) by including thesemantic similarity between the features terms [5]. This new model has improved the clustering quality in terms of compactness (highintra-cluster similarity) and clearness (low inter-cluster similarity). Second, we optimize the clustering ensemble process with the helpof GA using a novel approach of the fitness function. We define a new measure, Pre-Paired Percentage (PPP), to be used as the fitnessfunction during the genetic cycle for optimization of clustering ensemble process [7]. Third, the most important and crucial step of theGA is to define the genetic operators, crossover and mutation. We express these operators by an intelligent mechanism of clusteringensemble. This approach has produced more logical offspring solutions [9]. Above stated all three contributions have shown remarkableresults in their corresponding areas. Experiments on real world social-web data have been performed to validate our new incrementalnovelties [8]

Photoacoustic-based thermal image formation and optimization using an evolutionary genetic algorithm

Abstract Introduction For improved efficiency and security in heat application during hyperthermia, it is important to monitor tissue temperature during treatments. Photoacoustic (PA) pressure wave amplitude has a temperature dependence given by the Gruenesein parameter. Consequently, changes in PA signal amplitude carry information about temperature variation in tissue. Therefore, PA has been proposed as an imaging technique to monitor temperature during hyperthermia. However, no studies have compared the performance of different algorithms to generate PA-based thermal images. Methods Here, four methods to estimate variations in PA signal amplitude for thermal image formation were investigated: peak-to-peak, integral of the modulus, autocorrelation of the maximum value, and energy of the signal. Changes in PA signal amplitude were evaluated using a 1-D window moving across the entire image. PA images were acquired for temperatures ranging from 36oC to 41oC using a phantom immersed in a temperature controlled thermal bath. Results The results demonstrated that imaging processing parameters and methods involved in tracking variations in PA signal amplitude drastically affected the sensitivity and accuracy of thermal images formation. The sensitivity fluctuated more than 20% across the different methods and parameters used. After optimizing the parameters to generate the thermal images using an evolutionary genetic algorithm (GA), the percentage of pixels within the acceptable error was improved, in average, by 7.5%. Conclusion Optimization of processing parameters using GA could increase the accuracy of measurement for this experimental setup and improve quality of PA-based thermal images.

Research on slotting optimization of medical devices warehouse in class A tertiary hospital / 医疗卫生装备

Objecive To explore modern management and optimization of hospital warehouse slotting with some class A tertiary hospital taken as an example.Methods EIQ_ABC classification method,multi-objective assignment model and genetic algorithm were used to analyze and simulate the data of 126 kinds of medical devices for 20 orders.Results The medical devices were divided into nine categories of Ⅰ A,Ⅰ B,Ⅰ C,Ⅱ] A,Ⅱ B,Ⅱ C,Ⅲ A,Ⅲ B and Ⅲ C.It's suggested that emphasized management and precision slotting storage were carried out for A categories of devices,dedicated storage for B categories and random storage for C categories.At the same time,the layout of the warehouse was optimized according to the importance of the goods so that the important goods were placed near the passageway.Slotting analog simulation diagram was obtained based on multi-objective assignment model of Ⅲ A category medical device.Conclusion Classified management and allocation as well as slotting optimization of medical devices contribute to improving hospital modern management in efficiency,cost and error rate.

Study on Forecasting Ceramic Membrane Fouling in TCM Extracts Based on Improved BP Neural Network / 中国中医药信息杂志

Objective To prevent and treat of ceramic membrane purification of membrane fouling process of TCM extracts; To explore new methods of forecasting membrane fouling degree.Methods BP neural network model was improved. Methods to fast determine the optimal number of neurons in the hidden layer and fast algorithm for optimizing the weight and threshold of BP neural network were studied. Data of 207 groups of TCM extracts were under network training and prediction.ResultsCompared with the models of multiple regression analysis, basic BP neural network and RBF neural network, the error of the improved BP neural network model was less than that of the BP neural network model, and the mean square error was only 0.0057. In addition, the improved BP neural network model performance was more stable. In the 20 random running experiments, the goal of the success rate achieved up to 95%.Conclusion The improved model has a good network performance, the fitting effect and prediction ability, and can forecast the fouling degree of membrane stably and accurately.

A research for scheduling method of rehabilitation medical resource for smart traditional Chinese medicine based on genetic algorithm and its application on the treatment for dysphagia / 中国医学装备

Objective:To explore a scheduling method of rehabilitation medical resource for smart traditional Chinese medicine(TCM) for dysphagia because of cerebral apoplexy in order to save diagnosis time for patient and reasonably arrange treatment process for medical personnel.Methods: We designed the framework of smart TCM rehabilitation system, and proposed the medical resource scheduling model including acupuncture, massage and rehabilitation training. In addition, the genetic algorithm was employed to establish the scheduling method under the optimal objective towards the scheduling time.Results: (1) The treatment time of five dysphagia patients by using rehabilitation resource scheduling in Beijing Zhongguancun Hospital were saved 42.5% from the total treatment time compared to without scheduling; (2)The rehabilitation process of twenty virtual dysphagia patients were treated by the simulation scheduling, and 71% of total treatment time was saved. The efficiency of diagnosis and treatment was improved obviously .Conclusion: Smart TCM rehabilitation resource scheduling method can be used in an assisted rehabilitation therapy for dysphagia because of cerebral apoplexy, and it can improve the efficiency of diagnosis and treatment for patient and save a lot of medical resources.