scholarly journals Computer Management Design and Optimization of City Smart Medical Laboratory Service

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiangdong Jin ◽  
Xia Zhang ◽  
Tianli Fan ◽  
Yinsen Song
Keyword(s):  
Neural Network ◽  
High Efficiency ◽  
Rbf Neural Network ◽  
Optimal Solution ◽  
Fast Response ◽  
Medical Laboratory ◽  
Laboratory Research ◽  
Laboratory Service ◽  
Design And Optimization ◽  
Laboratory Services

In order to optimize the computer management of smart medical laboratory services and find the optimal solution, we conducted experiments on the laboratory computers of hospitals in this city based on the RBF neural network, which provided references for other researchers. Through the collection of relevant data, this article summarizes and analyzes the existing medical laboratory research, summarizes the existing problems and development directions of the current laboratory, uses the RBF neural network to modify these models, and innovatively achieves a hospital laboratory computer management optimization system with the characteristics of high efficiency, low energy consumption, and fast response. The experimental results prove that the computer management and optimization of laboratory services are optimized through the RBF neural network, and the efficiency of computer management design and optimization is greatly improved. It is about 20% higher than traditional medical laboratory. This shows that the computer management design and optimization of smart medical laboratory services designed by RBF neural network can play an important role in the construction of hospital laboratories.

Organization of the work of the medical laboratory service in accordance with the requirements of the standard GOST ISO 15189–2015 «Medical laboratories. Particular requirements for quality and competence»

2020 ◽  
pp. 15-22
Author(s):  
Elena Vitalievna Perminova
Keyword(s):  
Clinical Laboratory ◽  
Medical Laboratory ◽  
Laboratory Research ◽  
Laboratory Diagnostics ◽  
Clinical Diagnostic Laboratory ◽  
Diagnostic Laboratory ◽  
Iso 15189 ◽  
Laboratory Service ◽  
Medical Laboratories

Clinical laboratory diagnostics is a medical specialty, which is based on in vitro diagnostic studies of biomaterial obtained from an individual. At the present stage, there are three main types of organization of the laboratory research process — a laboratory service as part of a medical and preventive institution, a centralized laboratory where biomaterials are delivered for research from various healthcare institutions, as well as mobile laboratories that allow conducting the research directly at the patient’s bedside. This discipline involves the use of a wide variety of diagnostic research methods and the use of a huge number of specific techniques. Their list should include carrying out hematological, microbiological, virological, immunological, serological, parasitic, and biochemical studies. Also, when organizing laboratory diagnostic activities, a number of other studies (cytological, histological, toxicological, genetic, molecular biological, etc.) are provided. A laboratory report is formulated after obtaining clinical data and comparing them with the obtained test results. The quality of laboratory tests is ensured through the systematic implementation of internal laboratory control, as well as participation in a national program for external quality assessment. The activities of the clinical diagnostic laboratory should be organized in accordance with the requirements of the standard GOST R ISO 15189–2015 «Medical laboratories. Particular requirements for quality and competence», which is based on the provisions of two more fundamental standards — ISO 9001 and ISO 17025, and adds a number of special requirements related to medical laboratories.

Hybrid Data Mining Approach for Image Segmentation Based Classification

Biometrics ◽  
2017 ◽  
pp. 1543-1561 ◽  
Author(s):  
Mrutyunjaya Panda ◽  
Aboul Ella Hassanien ◽  
Ajith Abraham
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Image Segmentation ◽  
Search Algorithm ◽  
Rbf Neural Network ◽  
Harmony Search ◽  
Optimal Solution ◽  
Solution Space ◽  
Harmony Search Algorithm ◽  
Data Mining Technique

Evolutionary harmony search algorithm is used for its capability in finding solution space both locally and globally. In contrast, Wavelet based feature selection, for its ability to provide localized frequency information about a function of a signal, makes it a promising one for efficient classification. Research in this direction states that wavelet based neural network may be trapped to fall in a local minima whereas fuzzy harmony search based algorithm effectively addresses that problem and able to get a near optimal solution. In this, a hybrid wavelet based radial basis function (RBF) neural network (WRBF) and feature subset harmony search based fuzzy discernibility classifier (HSFD) approaches are proposed as a data mining technique for image segmentation based classification. In this paper, the authors use Lena RGB image; Magnetic resonance image (MR) and Computed Tomography (CT) Image for analysis. It is observed from the obtained simulation results that Wavelet based RBF neural network outperforms the harmony search based fuzzy discernibility classifiers.

Analysis of Speed Servo System of Pneumatic Manipulator Based RBF Neural Network PID Control

2010 ◽  
Vol 40-41 ◽  
pp. 65-70 ◽  
Author(s):  
Jing Luo ◽  
Rui Bo Yuan ◽  
Yu Bi Yuan ◽  
Shao Nan Ba ◽  
Zong Cheng Zhang
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Hybrid Control ◽  
Rbf Neural Network ◽  
Servo System ◽  
Fast Response ◽  
Quick Response ◽  
Pneumatic Servo System ◽  
The Stability ◽  
Neural Network Pid

Through analysis and comparison of simple PID control and RBF neural network-PID hybrid control of the pneumatic servo system, then compared the stability and quick response under the two control system. Concluded that RBF neural network-PID hybrid control has better stability and fast response than the simple PID control.

scholarly journals Study on Intelligent Speed Control Algorithm for Diesel Engine

2018 ◽  
Author(s):  
C Ma ◽  
G Zhao ◽  
C Yao ◽  
E Song
Keyword(s):  
Neural Network ◽  
Diesel Engine ◽  
Rbf Neural Network ◽  
Speed Control ◽  
Fast Response ◽  
Good Effect ◽  
Control Effect ◽  
Neural Network Controller ◽  
Load Change ◽  
Adaptive Capabilities

In this paper, two types of intelligent controllers are designed based on the RBF neural network algorithm and active disturbance rejection control (ADRC) technology to solve the problem that the dynamic speed is difficult to control for diesel engine. In order to verify the speed regulation performance of the intelligent control system a mean value modeling (MVM) of D6114 generation diesel engine was established for off-line simulation, and the above two intelligent algorithms were compared with PID. The results show that the ADRC has a relatively small overshoot and quick dynamic response for diesel engine speed control. Radial basis function (RBF) intelligent algorithm can real-timely optimize the control parameters and has good adaptability in speed control, the transient rate decreased by 1.6% and stable time is shortened by 1.46s compared with common PID algorithm. The control performance under condition of start-up, idle speed and mutation load is compared. The results show that RBF neural network controller has good learning and adaptive capabilities for speed control of diesel engine. It can balance the stability at different speed and output of large rack displacement in a short time when the load changes to reduce the influence of load change on the rotational speed. For ADRC controller, it maintains good effect when the nonlinearity in the system increases. Improvement of PID using TD has fast response at startup and under disturbances. With NLSEF and ESO, NLSEF can automatically adjust the output according to the speed deviation to reduce interference while ESO can correct the control amount to improve the control effect of load change.

A systematic approach to billing laboratory services – the legal framework

2015 ◽  
Vol 38 (4) ◽  
Author(s):  
Jan Rathenberg ◽  
Boris Ivandic ◽  
Cornelia Wohlfart
Keyword(s):  
Business Processes ◽  
Human Genetics ◽  
Legal Framework ◽  
Medical Laboratory ◽  
Hospital Treatment ◽  
Care Hospital ◽  
Laboratory Service ◽  
Laboratory Services ◽  
Health Care Law ◽  
Great Complexity

AbstractReimbursement of medical laboratory services may initially seem self-explanatory to all involved – doctors, hospitals, and laboratories. However, recurrent questions on how and to whom a particular test has to be invoiced, or whether a particular mode of billing is correct, demonstrate the great complexity in this field. These and other related issues emerge if laboratory orders involve different laboratory sectors, medical tariffs, and invoice recipients. The correct invoice practice – often evident only at second glance – has fundamental legal and financial significance not just for the laboratory service provider but also for the medical client. Influencing nearly all business processes from order to invoice, the correct invoice practice deserves special attention and diligence. In this review, we cover all relevant aspects of billing medical laboratory services in the context of ambulatory care, hospital treatment, and other models on the basis of the German health care law. We focus not only on laboratory medicine and microbiology but also include human genetics and histopathology. In addition, a schematic representation of common rules of invoicing laboratory services is provided with references to the appropriate legal text sources.

scholarly journals The Application of RBF Neural Network Optimized by K-means and Genetic-backpropagation in Fault Diagnosis of Power Transformer

2021 ◽  
Vol 242 ◽  
pp. 03002
Author(s):  
Xinxin Mi ◽  
Gopinath Subramani ◽  
Mieowkee Chan
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Rbf Neural Network ◽  
Optimal Solution ◽  
Nonlinear Problems ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Training Data ◽  
Transformer Fault

Through the dissolved gas analysis (DGA) in transformer oil, the fault of the power transformer can be diagnosed. However, the DGA method has the disadvantage of low accuracy because it couldn’t exactly reflect the nonlinear relationship between the characteristic gases and fault types. Radial basis function neural network (RBFNN) has the advantage of dealing with complex nonlinear problems, so it can be applied to transformer fault diagnosis based on DGA. The centers, widths and weights has important effects on the performance of the RBFNN. However, it is difficult to find the global optimal solution of these parameters when RBFNN training. This paper creatively designs a method to improve these parameters of RBFNN, firstly using the K-means algorithm to optimize the centers and widths of RBFNN, then using the genetic algorithm-backpropagation (GA-BP) algorithm optimize the weights. Finally, establish the K-means RBF-genetic backpropagation (KRBF-GBP) algorithm model through a large amount of training data. The test results show that the fault diagnosis accuracy of the KRBF-GBP algorithm is 96.4%, higher than the unoptimized RBFNN with 71.43%.

Three-year customer satisfaction survey in laboratory medicine in a Chinese university hospital

2018 ◽  
Vol 56 (5) ◽  
pp. 755-763
Author(s):  
Siqi Guo ◽  
Yifei Duan ◽  
Xiaojuan Liu ◽  
Yongmei Jiang
Keyword(s):  
Customer Satisfaction ◽  
Turnaround Time ◽  
Influential Factors ◽  
University Hospital ◽  
Medical Laboratory ◽  
Laboratory Service ◽  
Laboratory Services ◽  
Essential Components ◽  
Customer Satisfaction Survey ◽  
Corrective Actions

AbstractBackground:Customer satisfaction is a key quality indicator of laboratory service. Patients and physicians are the ultimate customers in medical laboratory, and their opinions are essential components in developing a customer-oriented laboratory.Methods:A longitudinal investigation of customer satisfaction was conducted through questionnaires. We designed two different questionnaires and selected 1200 customers (600 outpatients and 600 physicians) to assess customer satisfaction every other year from 2012 to 2016. Items with scores <4 were considered unsatisfactory, and corrective actions should be taken.Results:The completion rates of physicians were 96.8% in 2012, 97% in 2014 and 96.5% in 2016, whereas the rates of patients were 95.3%, 96.2% and 95.2%, respectively. In 2012, the most dissatisfaction items were test turnaround time (3.77 points) and service attitude (3.87 points) from physicians, whereas waiting time (3.58 points) and examination environment (3.64 points) were the most dissatisfaction items from patients. After corrective actions were taken, the result of satisfaction in 2014 was better, which illustrated our strategy was effective. However, some items remained to be less than 4, so we repeated the survey after modifying questionnaires in 2016. However, the general satisfaction points of the physicians and patients reduced in 2016, which reminded us of some influential factors we had neglected.Conclusions:By using dynamic survey of satisfaction, we can continuously find deficiencies in our laboratory services and take suitable corrective actions, thereby improving our service quality.

scholarly journals Parameter Identification of Multistage Fracturing Horizontal Well Based on PSO-RBF Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Rongwang Yin ◽  
Qingyu Li ◽  
Peichao Li ◽  
Detang Lu
Keyword(s):  
Neural Network ◽  
Parameter Identification ◽  
Network Model ◽  
Neural Network Model ◽  
Horizontal Well ◽  
Rbf Neural Network ◽  
Optimal Solution ◽  
Absolute Error ◽  
Physical Parameters ◽  
Proposed Model

In order to more accurately identify multistage fracturing horizontal well (MFHW) parameters and address the heterogeneity of reservoirs and the randomness of well-production data, a new method based on the PSO-RBF neural network model is proposed. First, the GPU parallel program is used to calculate the bottomhole pressure of a multistage fracturing horizontal well. Second, most of the above pressure data are imported into the RBF neural network model for training. In the training process, the optimization function of the global optimal solution of the PSO algorithm is employed to optimize the parameters of the RBF neural network, and eventually, the required PSO-RBF neural network model is established. Third, the resulting neural network is tested using the remaining data. Finally, a field case of a multistage fracturing horizontal well is studied by using the presented PSO-RBF neural network model. The results show that in most cases, the proposed model performs better than other models, with the highest correlation coefficient, the lowest mean, and absolute error. This proves that the PSO-RBF neural network model can be applied effectively to horizontal well parameter identification. The proposed model has great potential to improve the prediction accuracy of reservoir physical parameters.

scholarly journals Development of a Prediction Model Based on RBF Neural Network for Sheet Metal Fixture Locating Layout Design and Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Zhongqi Wang ◽  
Bo Yang ◽  
Yonggang Kang ◽  
Yuan Yang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Sheet Metal ◽  
Simulation Analysis ◽  
Rbf Neural Network ◽  
Training Data ◽  
Data Set ◽  
Design And Optimization ◽  
Element Simulation ◽  
Neural Network Prediction

Fixture plays an important part in constraining excessive sheet metal part deformation at machining, assembly, and measuring stages during the whole manufacturing process. However, it is still a difficult and nontrivial task to design and optimize sheet metal fixture locating layout at present because there is always no direct and explicit expression describing sheet metal fixture locating layout and responding deformation. To that end, an RBF neural network prediction model is proposed in this paper to assist design and optimization of sheet metal fixture locating layout. The RBF neural network model is constructed by training data set selected by uniform sampling and finite element simulation analysis. Finally, a case study is conducted to verify the proposed method.

Combined Forecasting Model on Automotive Logistics Demand Based on RBF Neural Network

2010 ◽  
Vol 148-149 ◽  
pp. 515-518
Author(s):  
Fei Fei Gao ◽  
Ji Chen Fang ◽  
Qiang Zhang ◽  
Qin Zhang ◽  
Zhan Gen Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Dimensional Space ◽  
Demand Forecasting ◽  
Optimal Solution ◽  
Forecasting Model ◽  
Combination Forecasting ◽  
Logistics Demand ◽  
The Stability ◽  
Combined Forecasting

This paper establishes a combination forecasting model based on Radia Basis Function Neural Network (RBFNN). It puts forward a seeking optimum parameters method by searching optimal solution for two-dimensional space (goal, spread) in a certain range, and realizes the combination forecasting of logistics demand, and improves the stability of network and the precision of prediction in RBFNN. An instance is presented to realize the model by MATLAB. The results showed that a good fitting precision and a high forecasting precision are reached in the application of the logistics demand forecasting by the designed forecasting model.

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