Adoption of network and plan-do-check-action in the international classification of disease 10 coding

BACKGROUND with the widespread application of computer network systems in the medical field,the plan-do-check-action(PDCA)and the international classification of diseases tenth edition(ICD-10)coding system have also achieved favorable results in clinical medical record management.However,research on their combined application is relatively lacking.Objective:it was to explore the impact of network systems and PDCA management mode on ICD-10 encoding.Material and Method:a retrospective collection of 768 discharged medical records from the Medical Record Management Department of Meishan People’s Hospital was conducted.They were divided into a control group(n=232)and an observation group(n=536)based on whether the PDCA management mode was implemented.The two sets of coding accuracy,time spent,case completion rate,satisfaction,and other indicators were compared.AIM To study the adoption of network and PDCA in the ICD-10.METHODS A retrospective collection of 768 discharged medical records from the Medical Record Management Department of Meishan People’s Hospital was conducted.They were divided into a control group(n=232)and an observation group(n=536)based on whether the PDCA management mode was implemented.The two sets of coding accuracy,time spent,case completion rate,satisfaction,and other indicators were compared.RESULTS In the 3,6,12,18,and 24 months of PDCA cycle management mode,the coding accuracy and medical record completion rate were higher,and the coding time was lower in the observation group as against the controls(P<0.05).The satisfaction of coders(80.22%vs 53.45%)and patients(84.89%vs 51.72%)in the observation group was markedly higher as against the controls(P<0.05).CONCLUSION The combination of computer networks and PDCA can improve the accuracy,efficiency,completion rate,and satisfaction of ICD-10 coding.

Analysis of Effluent Outfall Problems and Their Classification and Regulation Countermeasures

Effluent outfalls are an important exit for pollutants discharged from the source flowing into environmental water bodies,as well as an important guarantee for the ecological environment of natural water bodies.In response to main problems of large and diverse effluent outfalls,as well as their monitoring analysis,tracing and regulation in China,classification and regulation countermeasures were proposed based on the characteristics of effluent outfalls.It is suggested that a comprehensive management and control system should be built by improving the management and control system,upgrading monitoring techniques and strengthening social supervision and public education,so as to provide a scientific basis for the supervision and management of effluent outfalls in China and help promote the improvement of water quality and the sustainable development and utilization of water resources.

Combined hepatocellular-cholangiocarcinoma:An update on epidemiology,classification,diagnosis and management

Background:Combined hepatocellular-cholangiocarcinoma(CHC)is a rare subtype of primary hepatic malignancies,with variably reported incidence between 0.4%–14.2%of primary liver cancer cases.This study aimed to systematically review the epidemiological,clinicopathological,diagnostic and therapeutic data for this rare entity.Data sources:We reviewed the literature of diagnostic approach of CHC with special reference to its clinical,molecular and histopathological characteristics.Additional analysis of the recent literature in order to evaluate the results of surgical and systemic treatment of this entity has been accomplished.Results:The median age at CHC’s diagnosis appears to be between 50 and 75 years.Evaluation of tumor markers[alpha fetoprotein(AFP),carbohydrate antigen 19–9(CA19–9)and carcinoembryonic antigen(CEA)]along with imaging patterns provides better opportunities for CHC’s preoperative diagnosis.Reported clinicopathologic prognostic parameters possibly correlated with increased tumor recurrence and grimmer survival odds include advanced age,tumor size,nodal and distal metastases,vascular and regional organ invasion,multifocality,decreased capsule formation,stem-cell features verification and increased GGT as well as CA19–9 and CEA levels.In case of inoperable or recurrent disease,combinations of cholangiocarcinoma-directed systemic agents display superior results over sorafenib.Liver-directed methods,such as transarterial chemoembolization(TACE),percutaneous ethanol injection(PEI),hepatic arterial infusion chemotherapy(HAIC),radioembolization and ablative therapies,demonstrate inferior efficacy than in cases of hepatocellular carcinoma(HCC)due to CHC’s common hypovascularity.Conclusions:CHC demonstrates an overlapping clinical and biological pattern between its malignant ingredients.Natural history of the disease seems to be determined by the predominant tumor element.Gold standard for diagnosis is histology of surgical specimens.Regarding therapeutic interventions,major hepatectomy is acknowledged as the cornerstone of treatment whereas minor hepatectomy and liver transplantation may be applied in patients with advanced cirrhosis.Despite all therapeutic attempts,prognosis of CHC remains dismal.

Diabetic foot ulcers:Classification,risk factors and management

Diabetic foot ulceration is a devastating complication of diabetes that is associated with infection,amputation,and death,and is affecting increasing numbers of patients with diabetes mellitus.The pathogenesis of foot ulcers is complex,and different factors play major roles in different stages.The refractory nature of foot ulcer is reflected in that even after healing there is still a high recurrence rate and amputation rate,which means that management and nursing plans need to be considered carefully.The importance of establishment of measures for prevention and management of DFU has been emphasized.Therefore,a validated and appropriate DFU classification matching the progression is necessary for clinical diagnosis and management.In the first part of this review,we list several commonly used classification systems and describe their application conditions,scope,strengths,and limitations;in the second part,we briefly introduce the common risk factors for DFU,such as neuropathy,peripheral artery disease,foot deformities,diabetes complications,and obesity.Focusing on the relationship between the risk factors and DFU progression may facilitate prevention and timely management;in the last part,we emphasize the importance of preventive education,characterize several of the most frequently used management approaches,including glycemic control,exercise,offloading,and infection control,and call for taking into account and weighing the quality of life during the formulation of treatment plans.Multidisciplinary intervention and management of diabetic foot ulcers(DFUs)based on the effective and systematic combination of these three components will contribute to the prevention and treatment of DFUs,and improve their prognosis.

Poorly cohesive cells gastric carcinoma including signet-ring cell cancer:Updated review of definition,classification and therapeutic management

While the incidence of gastric cancer(GC)in general has decreased worldwide in recent decades,the incidence of diffuse cancer historically comprising poorly cohesive cells-GC(PCC-GC)and including signet ring cell cancer is rising.Literature concerning PCC-GC is scarce and unclear,mostly due to a large variety of historically used definitions and classifications.Compared to other histological subtypes of GC,PCC-GC is nevertheless characterized by a distinct set of epidemiological,histological and clinical features which require a specific diagnostic and therapeutic approach.The aim of this review was to provide an update on the definition,classification and therapeutic strategies of PCC-GC.We focus on the updated histological definition of PCC-GC,along with its implications on future treatment strategies and study design.Also,specific considerations in the diagnostic management are discussed.Finally,the impact of some recent developments in the therapeutic management of GC in general such as the recently validated taxane-based regimens(5-Fluorouracil,leucovorin,oxaliplatin and docetaxel),the use of hyperthermic intraperitoneal chemotherapy as well as pressurized intraperitoneal aerosol chemotherapy and targeted therapy have been reviewed in depth for their relative importance for PCC-GC in particular.

Classification and Management of Mandibular Condyle Fractures in a Tertiary Health Center

Background: Condyle fractures are not common but could lead to detrimental effects of growth disturbance of the mandible, ankylosis of temporomandibular joint and facial asymmetry especially in children, if not promptly and adequately managed, the aim of this study was to document our experience in the management of mandibular condyle fractures. Method: The fractures were classified based on the age of the patient, unilateral/bilateral, location on the condyle, presence of displacement and dislocation, for those displaced, whether there was medial or lateral overlap, and features presented. Treatment done for each patient was documented. Both clinical and radiological assessments were done to ascertain the outcome of treatment. Result: 11 patients presented with 14 condyle fractures, 3 patients with bilateral and 8 with unilateral condyle fractures out of which 5 cases were on the right side. Age range of patients was between 13 and 44 years with a mean (SD) of 25.3 (10.7) years. Nine (81.8%) of the patients were males and 2 (18.2%) were females. Eight (72.7%) of the patients with condyle fracture had associated fractures affecting other sites of the mandible while 3 (27.3%) patients had isolated condyle fractures. Intracapusular fractures recorded were 2 (14.2%), while extracapsular accounted for 12 (85.8%) cases. Conservative treatment was not applied in any patient, 9 (81.8%) patients had IMF and 2 (18.2%) patients had ORIF. Conclusion: Most fractures of the condyle were extracapsular and, closed surgical treatment (IMF) was very useful to manage most of the cases.

New Thinking of Traditional Industry Development under the New Economic Condition——Classification of the Industry by the Management Mode of Nike

This paper has announced the arrival of new economic era through an analysis of Nike＇s management mode. The traditional industry classification can＇t meet demands of industry development. We should inherit and improve traditional economy in order to adapt to the development demand of new economy.

Study on Customer Demand Forecasting Models,Stock Management,Classification and Policies for Automobile Parts Manufacturing Company N.A.C.C.(An Advance on Classical Models)

The primary intent of the current research is to provide insights regarding the management of spare parts within the supply chain,in conjunction with offering some methods for enhancing forecasting and inventory management.In particular,to use classical forecasting methods,the use of weak and unstable demand is not recommended.Furthermore,statistical performance measures are not involved in this particular context.Furthermore,it is expected that maintenance contracts will be aligned with different levels.In addition to the examination of some literature reviews,some tools will guide us through this process.The article proposes new performance analysis methods that will help integrate inventory management and statistical performance while considering decision maker priorities through the use of different methodologies and parts age segmentation.The study will also identify critical level policies by comparing different types of spenders according to the inventory management model,also with separate and common inventory policies.Each process of the study is combined with a comparative analysis of different forecasting methods and inventory management models based on N.A.C.C.parts supply chain data,allowing us to identify a set of methodologies and parameter recommendations based on parts segmentation and supply chain prioritization.

Sparrow Search Optimization with Transfer Learning-Based Crowd Density Classification

Due to the rapid increase in urbanization and population,crowd gatherings are frequently observed in the form of concerts,political,and religious meetings.HAJJ is one of the well-known crowding events that takes place every year in Makkah,Saudi Arabia.Crowd density estimation and crowd monitoring are significant research areas in Artificial Intelligence(AI)applications.The current research study develops a new Sparrow Search Optimization with Deep Transfer Learning based Crowd Density Detection and Classification(SSODTL-CD2C)model.The presented SSODTL-CD2C technique majorly focuses on the identification and classification of crowd densities.To attain this,SSODTL-CD2C technique exploits Oppositional Salp Swarm Optimization Algorithm(OSSA)with EfficientNet model to derive the feature vectors.At the same time,Stacked Sparse Auto Encoder(SSAE)model is utilized for the classification of crowd densities.Finally,SSO algorithm is employed for optimal fine-tuning of the parameters involved in SSAE mechanism.The performance of the proposed SSODTL-CD2C technique was validated using a dataset with four different kinds of crowd densities.The obtained results demonstrated that the proposed SSODTLCD2C methodology accomplished an excellent crowd classification performance with a maximum accuracy of 93.25%.So,the proposed method will be highly helpful in managing HAJJ and other crowded events.

Entropy Based Feature Fusion Using Deep Learning for Waste Object Detection and Classification Model

Object Detection is the task of localization and classification of objects in a video or image.In recent times,because of its widespread applications,it has obtained more importance.In the modern world,waste pollution is one significant environmental problem.The prominence of recycling is known very well for both ecological and economic reasons,and the industry needs higher efficiency.Waste object detection utilizing deep learning(DL)involves training a machine-learning method to classify and detect various types of waste in videos or images.This technology is utilized for several purposes recycling and sorting waste,enhancing waste management and reducing environmental pollution.Recent studies of automatic waste detection are difficult to compare because of the need for benchmarks and broadly accepted standards concerning the employed data andmetrics.Therefore,this study designs an Entropy-based Feature Fusion using Deep Learning forWasteObject Detection and Classification(EFFDL-WODC)algorithm.The presented EFFDL-WODC system inherits the concepts of feature fusion and DL techniques for the effectual recognition and classification of various kinds of waste objects.In the presented EFFDL-WODC system,two major procedures can be contained,such as waste object detection and waste object classification.For object detection,the EFFDL-WODC technique uses a YOLOv7 object detector with a fusionbased backbone network.In addition,entropy feature fusion-based models such as VGG-16,SqueezeNet,and NASNetmodels are used.Finally,the EFFDL-WODC technique uses a graph convolutional network(GCN)model performed for the classification of detected waste objects.The performance validation of the EFFDL-WODC approach was validated on the benchmark database.The comprehensive comparative results demonstrated the improved performance of the EFFDL-WODC technique over recent approaches.

Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops

Crop insect detection becomes a tedious process for agronomists because a substantial part of the crops is damaged,and due to the pest attacks,the quality is degraded.They are the major reason behind crop quality degradation and diminished crop productivity.Hence,accurate pest detection is essential to guarantee safety and crop quality.Conventional identification of insects necessitates highly trained taxonomists to detect insects precisely based on morphological features.Lately,some progress has been made in agriculture by employing machine learning(ML)to classify and detect pests.This study introduces a Modified Metaheuristics with Transfer Learning based Insect Pest Classification for Agricultural Crops(MMTL-IPCAC)technique.The presented MMTL-IPCAC technique applies contrast limited adaptive histogram equalization(CLAHE)approach for image enhancement.The neural architectural search network(NASNet)model is applied for feature extraction,and a modified grey wolf optimization(MGWO)algorithm is employed for the hyperparameter tuning process,showing the novelty of the work.At last,the extreme gradient boosting(XGBoost)model is utilized to carry out the insect classification procedure.The simulation analysis stated the enhanced performance of the MMTL-IPCAC technique in the insect classification process with maximum accuracy of 98.73%.

Endoscopic retrograde cholangiopancreatography-related perforation: Management and prevention

Endoscopic retrograde cholangiopancreatography(ERCP) is a procedure that can result in serious complications, and thus should be handled by a skilled endoscopist to minimize the risk of complications and to enhance the success rate. The incidence of ERCP-related complications is 5%-10%, most commonly involving post-ERCP pancreatitis and clinically significant post-endoscopic sphincterotomy bleeding. Although ERCP-related perforation has a relatively lower incidence of 0.14%-1.6%, this complication is associated with a high mortality rate of 4.2%-29.6%. A classification of perforation type based on the instrument that caused the perforation was recently described that we postulated could affect the implementation of perforation management. In the present article, an algorithm for management and prevention of ERCP-related perforations is proposed that is based on the perforation type and delay of diagnosis. Available evidence demonstrates that a delayed diagnosis and/or treatment of perforation re-sults in a poorer prognosis, and thus should be at the forefront of procedural consideration. Furthermore, this review provides steps and recommendations from the pre-procedural stage through the post-procedural evaluation with consideration of contributing factors in order to minimize ERCP-related complication risk and improve patient outcome. To avoid perforation, endoscopists must evaluate the risks related to the individual patient and the procedure and perform the procedure gently. Once a perforation occurs, immediate diagnosis and early management are key factors to minimize mortality.

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang County, a typical landslide-prone area located in northwestern China, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data(70% landslide pixels) and validation data(30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC) curve) of the proposed model was the highest, reaching 0.88,compared with traditional models(support vector machine(SVM) = 0.85, Bayesian network(BN) = 0.81,frequency ratio(FR) = 0.75, weight of evidence(WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km^(2) and 0.88/km^(2), respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without containing more " stable" pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices.

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone to attacks.Various management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic classification.Due to the ineffectiveness of traditional classification schemes,such as port-based and payload-based methods,researchers proposed machine learning-based traffic classification systems based on shallow neural networks.Furthermore,machine learning-based models incline to misclassify internet traffic due to improper feature selection.In this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic.To examine the performance of the proposed technique,Moore-dataset is used for training the classifier.The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network(DNN).In particular,the maximum entropy classifier is used to classify the internet traffic.The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification,i.e.,99.23%.Furthermore,the proposed algorithm achieved the highest accuracy compared to the support vector machine(SVM)based classification technique and k-nearest neighbours(KNNs)based classification technique.

Management Modes at Different Levels and Management Effectiveness of Nature Reserve

Classification management is one of nature reserves management system in China. But state nature reserves and local administration nature reserves under the regulations are only the approval system and embody the concept of protection. Management pattern of nature reserves can be divided into nine types. There are big differences in the nine patterns in management foundation and coordination ability of management because different management pattern occupies different resources of administration,personnel,financial and law enforcement. By analyzing management pattern,thirty-eight indexes in thirteen categories were selected to evaluate the management effectiveness of national nature reserves subordinate to the State Forestry Administration (SFA) . Results show that the management effectiveness of national nature reserves is good as a whole,and the management effectiveness is direct proportional to administration level. Provincial administration has the higher efficiency than municipal and county administration. Direct administration by governments at all levels has the higher efficiency than departments' administration at the same level.

Automated Building Block Extraction and Building Density Classification Using Aerial Imagery and LiDAR Data

This paper examines the utility of high-resolution airborne RGB orthophotos and LiDAR data for mapping residential land uses within the spatial limits of suburb of Athens, Greece. Modem remote sensors deliver ample information from the AOI （area of interest） for the estimation of 2D indicators or with the inclusion of elevation data 3D indicators for the classification of urban land. In this research, two of these indicators, BCR （building coverage ratio） and FAR （floor area ratio） are automatically evaluated. In the pre-processing step, the low resolution elevation data are fused with the high resolution optical data through a mean-shift based discontinuity preserving smoothing algorithm. The outcome is an nDSM （normalized digital surface model） comprised of upsampled elevation data with considerable improvement regarding region filling and ＂straightness＂ of elevation discontinuities. Following this step, a MFNN （multilayer feedforward neural network） is used to classify all pixels of the AOI into building or non-building categories. The information derived from the BCR and FAR building indicators, adapted to landscape characteristics of the test area is used to propose two new indices and an automatic post-classification based on the density of buildings.

Olive Mill Waste Water Management Study by Using Principal Component Analysis

Olive mill waste water (OMWW) is a by-product issued after triturating olives. In Sfax, its management is different from urban to farming area. In this paper we treat it through a statistical analysis study during the season 2005-2006. Principal Component Analysis (PCA) and Hierarchical Classification (HC) methods are carried out on this work. Applied to variables issued from an exhaustive questionnaire including 274 mills, four Principal Components (PCs) are found to be significant, explaining 67% of the total variance. The coordinates of the 13 active variables retained by PCA were used to create a typology relative to the OMWW management and offered 7 groups of individuals which have the same characteristics, explaining 70% of the total inter-variance. This study showed that OMWW management in farming area could causes environmental problems because oleifactors haven’t controlled tanks and could evacuated OMWW on soil (causing oil deposit, waterproofing and possible asphyxia) or on public sewage network (causing corrosion, flow reduction). So, mills transfer from urban to farming areas in the form of agro-industrial complex is needed in the Sfax region.

Deep neural network based classification of rolling element bearings and health degradation through comprehensive vibration signal analysis

Rolling element bearings are machine components used to allow circular movement and hence deliver forces between components of machines used in diverse areas of industry.The likelihood of failure has the propensity of increasing under prolonged operation and varying working conditions.Hence, the accurate fault severity categorization of bearings is vital in diagnosing faults that arise in rotating machinery.The variability and complexity of the recorded vibration signals pose a great hurdle to distinguishing unique characteristic fault features.In this paper, the efficacy and the leverage of a pre-trained convolutional neural network(CNN) is harnessed in the implementation of a robust fault classification model.In the absence of sufficient data, this method has a high-performance rate.Initially, a modified VGG16 architecture is used to extract discriminating features from new samples and serves as input to a classifier.The raw vibration data are strategically segmented and transformed into two representations which are trained separately and jointly.The proposed approach is carried out on bearing vibration data and shows high-performance results.In addition to successfully implementing a robust fault classification model, a prognostic framework is developed by constructing a health indicator(HI) under varying operating conditions for a given fault condition.

Developing Ecosystem Maps Using Eco-Geological Information for the Sustainable Management of Natural Resources

Ecosystem maps are vital to represent ecological patterns and processes in a region. It enables the use of ecosystem goods and services as a robust unit for a variety of applications, including conservation planning, climate change adaptation and mitigation measures, resource management, and the economic estimation of ecosystem benefits. As different elements of eco-geological components, such as the geological, soil, and biotic assemblages organize an ecosystem;here, we developed an ecosystem map of the State of Selangor, Peninsular Malaysia, using geology, soil, elevation, and land-use data. Landsat ETM+ images, ASTER Digital Elevation Model (DEM) data, and digitized soil and geological data were integrated to develop a map of the types of ecosystem for 2005. We found 19 different natural ecosystems in the studied region that represented approximately 35% of the total land area. Among the natural ecosystems, peat-swamp forest represents highest (at ~10%), while montane ericaceous forest representing the lowest (at ~0.008%) and the hill dipterocarp quartz forest, lowland dipterocarp sandstone forest, upper dipterocarp quartz forest, and mangrove forest are representing approximately 6.4%, 4.6%, 3% and 2.6% of the total land area respectively. These data can be used to prioritize the areas deserving attention due to their value for biodiversity conservation and for the production of goods and supply of ecosystem services. In addition to a variety of ecological and environmental aspects, such an ecosystem map has potential use for the sustainable management of natural resources at the national, regional, continental, and global scales.

EDCMS:A Content Management System for Engineering Documents

Engineers often need to look for the right pieces of information by sifting through long engineering documents, It is a very tiring and time-consuming job. To address this issue, researchers are increasingly devoting their attention to new ways to help information users, including engineers, to access and retrieve document content. The research reported in this paper explores how to use the key technologies of document decomposition （study of document structure）, document mark-up （with EXtensible Mark- up Language （XML）, HyperText Mark-up Language （HTML）, and Scalable Vector Graphics （SVG））, and a facetted classification mechanism. Document content extraction is implemented via computer programming （with Java）. An Engineering Document Content Management System （EDCMS） developed in this research demonstrates that as information providers we can make document content in a more accessible manner for information users including engineers.The main features of the EDCMS system are： 1） EDCMS is a system that enables users, especially engineers, to access and retrieve information at content rather than document level. In other words, it provides the right pieces of information that answer specific questions so that engineers don＇t need to waste time sifting through the whole document to obtain the required piece of information. 2） Users can use the EDCMS via both the data and metadata of a document to access engineering document content. 3） Users can use the EDCMS to access and retrieve content objects, i.e. text, images and graphics （including engineering drawings） via multiple views and at different granularities based on decomposition schemes. Experiments with the EDCMS have been conducted on semi-structured documents, a textbook of CADCAM, and a set of project posters in the Engineering Design domain. Experimental results show that the system provides information users with a powerful solution to access document content.