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.

Lightweight Malicious Code Classification Method Based on Improved Squeeze Net

With the growth of the Internet,more and more business is being done online,for example,online offices,online education and so on.While this makes people’s lives more convenient,it also increases the risk of the network being attacked by malicious code.Therefore,it is important to identify malicious codes on computer systems efficiently.However,most of the existing malicious code detection methods have two problems:(1)The ability of the model to extract features is weak,resulting in poor model performance.(2)The large scale of model data leads to difficulties deploying on devices with limited resources.Therefore,this paper proposes a lightweight malicious code identification model Lightweight Malicious Code Classification Method Based on Improved SqueezeNet(LCMISNet).In this paper,the MFire lightweight feature extraction module is constructed by proposing a feature slicing module and a multi-size depthwise separable convolution module.The feature slicing module reduces the number of parameters by grouping features.The multi-size depthwise separable convolution module reduces the number of parameters and enhances the feature extraction capability by replacing the standard convolution with depthwise separable convolution with different convolution kernel sizes.In addition,this paper also proposes a feature splicing module to connect the MFire lightweight feature extraction module based on the feature reuse and constructs the lightweight model LCMISNet.The malicious code recognition accuracy of LCMISNet on the BIG 2015 dataset and the Malimg dataset reaches 98.90% and 99.58%,respectively.It proves that LCMISNet has a powerful malicious code recognition performance.In addition,compared with other network models,LCMISNet has better performance,and a lower number of parameters and computations.

Quantitative information measurement and application for machine component classification codes

Information embodied in machine component classification codes has internal relation with the probability distribu- tion of the code symbol. This paper presents a model considering codes as information source based on Shannon’s information theory. Using information entropy, it preserves the mathematical form and quantitatively measures the information amount of a symbol and a bit in the machine component classification coding system. It also gets the maximum value of information amount and the corresponding coding scheme when the category of symbols is fixed. Samples are given to show how to evaluate the information amount of component codes and how to optimize a coding system.

Open-Source Codes of Topology Optimization: A Summary for Beginners to Start Their Research

Topology optimization(TO),a numerical technique to find the optimalmaterial layoutwith a given design domain,has attracted interest from researchers in the field of structural optimization in recent years.For beginners,opensource codes are undoubtedly the best alternative to learning TO,which can elaborate the implementation of a method in detail and easily engage more people to employ and extend the method.In this paper,we present a summary of various open-source codes and related literature on TO methods,including solid isotropic material with penalization(SIMP),evolutionary method,level set method(LSM),moving morphable components/voids(MMC/MMV)methods,multiscale topology optimization method,etc.Simultaneously,we classify the codes into five levels,fromeasy to difficult,depending on their difficulty,so that beginners can get started and understand the form of code implementation more quickly.

Deep Belief Network for Lung Nodule Segmentation and Cancer Detection

Cancer disease is a deadliest disease cause more dangerous one.By identifying the disease through Artificial intelligence to getting the mage features directly from patients.This paper presents the lung knob division and disease characterization by proposing an enhancement calculation.Most of the machine learning techniques failed to observe the feature dimensions leads inaccuracy in feature selection and classification.This cause inaccuracy in sensitivity and specificity rate to reduce the identification accuracy.To resolve this problem,to propose a Chicken Sine Cosine Algorithm based Deep Belief Network to identify the disease factor.The general technique of the created approach includes four stages,such as pre-processing,segmentation,highlight extraction,and the order.From the outset,the Computerized Tomography(CT)image of the lung is taken care of to the division.When the division is done,the highlights are extricated through morphological factors for feature observation.By getting the features are analysed and the characterization is done dependent on the Deep Belief Network(DBN)which is prepared by utilizing the proposed Chicken-Sine Cosine Algorithm(CSCA)which distinguish the lung tumour,giving two classes in particular,knob or non-knob.The proposed system produce high performance as well compared to the other system.The presentation assessment of lung knob division and malignant growth grouping dependent on CSCA is figured utilizing three measurements to be specificity,precision,affectability,and the explicitness.

A Comparison of Error Correction Models for Student’s Error Codes Based on Deep Learning

Automatically correcting students’code errors using deep learning is an effective way to reduce the burden of teachers and to enhance the effects of students’learning.However,code errors vary greatly,and the adaptability of fixing techniques may vary for different types of code errors.How to choose the appropriate methods to fix different types of errors is still an unsolved problem.To this end,this paper first classifies code errors by Java novice programmers based on Delphi analysis,and compares the effectiveness of different deep learning models(CuBERT,GraphCodeBERT and GGNN)fixing different types of errors.The results indicated that the 3 models differed significantly in their classification accuracy on different error codes,while the error correction model based on the Bert structure showed better code correction potential for beginners’codes.

A survey of automated International Classification of Diseases coding:development,challenges,and applications

The International Classification of Diseases(ICD)is an international standard and tool for epidemiological in-vestigation,health management,and clinical diagnosis with a fundamental role in intelligent medical care.The assignment of ICD codes to health-related documents has become a focus of academic research,and numerous studies have developed the process of ICD coding from manual to automated work.In this survey,we review the developmental history of this task in recent decades in depth,from the rules-based stage,through the traditional machine learning stage,to the neural-network-based stage.Various methods have been introduced to solve this problem by using different techniques,and we report a performance comparison of different methods on the pub-licly available Medical Information Mart for Intensive Care dataset.Next,we summarize four major challenges of this task:(1)the large label space,(2)the unbalanced label distribution,(3)the long text of documents,and(4)the interpretability of coding.Various solutions that have been proposed to solve these problems are analyzed.Further,we discuss the applications of ICD coding,from mortality statistics to payments based on disease-related groups and hospital performance management.In addition,we discuss different ways of considering and evaluat-ing this task,and how it has been transformed into a learnable problem.We also provide details of the commonly used datasets.Overall,this survey aims to provide a reference and possible prospective directions for follow-up research work.

The Processing of Parts with Group Technology in Individual CNC Machining Center

This paper proposes a new machining method for part s using group technology suitable for workshop condition in China. The method divi des the parts of each product into similar kinds according to their features, an d installs each kind of parts on different working platforms. The working platfo rm, as a unit, is designed to be flexible and is interchangeable. It can be hung and fitted to the CNC working table. By changing the platforms in turn, nearly all the parts of one product can be processed with one CNC machine, and can be s ent directly to an assembly shop. Thus the number of management links is reduced and the production rate is increased. Several working platforms may be needed w hen several different types of parts are being processed. However, to reduce pro duction costs, it is necessary to limit the similarity coefficient of part class ification. The feature-coding classification is better for processing parts wit h this method. A practical classification code made by the author is for plastic injection machines. As an example, ten numbers (0～9) are used to express the d i fferent functions and the main features of the parts in the first code-rank. An insufficient classification may be offset by applying cut store and adjusting f unctions to the processing program in the CNC machine center, thereby allowing a set of parts which are not completely similar to be processed on a single worki ng platform. Before a set of parts with incompletely similar features is machine d on a working platform, it is necessary to plan reasonable technological proces s, for which we adopted an optimum method aimed to shorten the working period so as to increase production rate. The fixture planning for this processing method is also discussed in this paper. Modular fixture is recommended for mounting a set of parts on a working platform. In general, modular fixture has a basic boar d and a set of disassemble locating and clamping elements. Because the working p latform can be used directly as the main locating datum, it is unnecessary to de sign another basic board. However, if a working platform is to be used for diffe rent sets of parts, it is better to design it as a dowel-pin series basic board .

How Media Opinion Influences Imports in the US

Does public opinion influence US imports?Do countries with a good reputation export more to the US?And vice versa?Based on an extended trade gravity model,this paper employs news data from the GDELT database and US monthly import data to create an indicator of the influence of public opinion to examine the effects of US domestic public opinion on imports.Our research findings suggest that:(i)US public opinion influences US imports.Specifically,(ii)when public opinion turned negative during 2013-2017,it exerted a significantly negative effect on US imports;when public opinion was favorable during 2008-2012,it exerted an insignificantly positive effect on US imports.(iii)According to the pulse response function and variance decomposition,negative public opinion exerted a more significant and more lasting effect on US imports compared with positive public opinion.(iv)It can be discovered after further decomposing such effects on product categories that significant product heterogeneity exists in the public opinion effects on US imports:Complex and differentiated products are more influenced by negative public opinion while homogeneous and intermediate products are more influenced by positive public opinion.

Research on Classification of Malware Source Code

In the face threat of the Internet attack, malware classification is one of the promising solutions in the field of intrusion detection and digital forensics. In previous work, researchers performed dynamic analysis or static analysis after reverse engineering. But malware developers even use anti-virtual machine(VM) and obfuscation techniques to evade malware classifiers. By means of the deployment of honeypots, malware source code could be collected and analyzed. Source code analysis provides a better classification for understanding the purpose of attackers and forensics. In this paper, a novel classification approach is proposed, based on content similarity and directory structure similarity. Such a classification avoids to re-analyze known malware and allocates resources for new malware. Malware classification also let network administrators know the purpose of attackers. The experimental results demonstrate that the proposed system can classify the malware efficiently with a small misclassification ratio and the performance is better than virustotal.