Error Correction Rate Research Articles

A comparison of prism adaptation with terminal versus concurrent exposure on sensorimotor changes and spatial neglect

ABSTRACTPrism adaptation (PA) is a technique that induces a temporary change of the alignment between different reference frames. This technique has been shown to reduce many signs of unilateral spatial neglect (USN). Two procedures of prism adaptation have been used. In concurrent exposure participants can see their arm and hand during the movement trajectory, and during terminal exposure, participants can see only the most distal few centimetres. Because the two exposures elicit different proportions of visual and proprioceptive realignment, they could present different rehabilitation efficacies. We compared these procedures in 12 USN patients and 12 healthy participants who performed one session of PA with concurrent exposure and one session with terminal exposure. We compared the effects of the two exposure types on sensorimotor outcomes (visual subjective straight ahead, proprioceptive subjective straight ahead, open loop pointing, and error correction during exposure) and neglect outcomes. We found no significant differences in the effects of the two exposure types on sensorimotor performance of patients and controls, nor on patients’ neuropsychological outcomes. Compared with controls, USN patients showed a significant rightward bias in visual subjective straight ahead pre-adaptation, a slower rate of error correction during prism exposure, and some evidence that visual and proprioceptive shift could be influenced by their neglect. Based on these results, we recommend that prism adaptation be conducted with concurrent exposure for easiness of execution.

Effects of reward and punishment on learning from errors in smokers

BackgroundPunishing errors facilitates adaptation in healthy individuals, while aberrant reward and punishment sensitivity in drug-dependent individuals may change this impact. Many societies have institutions that use the concept of punishing drug use behavior, making it important to understand how drug dependency mediates the effects of negative feedback for influencing adaptive behavior. MethodsUsing an associative learning task, we investigated differences in error correction rates of dependent smokers, compared with controls. Two versions of the task were administered to different participant samples: One assessed the effect of varying monetary contingencies to task performance, the other, the presence of reward as compared to avoidance of punishment for correct performance. ResultsWhile smokers recalled associations that were rewarded with a higher value 11% more often than lower rewarded locations, they did not correct higher punished locations more often. Controls exhibited the opposite pattern. The three-way interaction between magnitude, feedback type and group was significant, F(1,48) = 5.288, p =0.026, ɳ2p =0.099. Neither participant group corrected locations offering reward more often than those offering avoidances of punishment. The interaction between group and feedback condition was not significant, F(1,58) = 0.0, p =0.99, ɳ2p =0.001. ConclusionsThe present results suggest that smokers have poorer learning from errors when receiving negative feedback. Moreover, larger rewards reinforce smokers' behavior stronger than smaller rewards, whereas controls made no distinction. These findings support the hypothesis that dependent smokers may respond to positively framed and rewarded anti-smoking programs when compared to those relying on negative feedback or punishment.

F20. SEX-SPECIFIC STRUCTURAL AND FUNCTIONAL CIRCUIT DIFFERENCES IN YOUTH WITH PSYCHOSIS SPECTRUM SYMPTOMS

BackgroundFunctional connectivity differences in the cortico-thalamic-striatal-cortical (CTSC) circuit, as well as altered subcortical region volumes have been observed in schizophrenia. In this study, structural and functional magnetic resonance imaging (MRI) were used in a large child and youth sample aged 11–21 years (n=1134) including children with psychosis spectrum (PS) symptoms (n=312) to further understanding of these biomarkers in youth outside of high risk groups and with a wider range of symptom severity.MethodsStructural subregions of the thalamus and striatum were identified using the segmentation tool MAGeT Brain. Functional subregions were segmented based on functional connectivity with the 7 functional networks identified in Yeo et al, 2011. Average time series from functional subregions were correlated vertex-wide with cortical surfaces and Fisher Z transformed. FSL’s PALM was used to examine differences and interactions between PS groups and sex. Age and in scanner motion (mean framewise displacement) were covaried for and a family wise error rate correction was applied. Structural subregion volume differences and interactions between PS groups and sex were investigated statistically using analyses of covariance (ANCOVA) with a false discovery rate of 5% correction for multiple testing. Age, intracranial volume, WRAT score and current medication use were covaried for.ResultsSex-specific differences between PS and non-PS youth in structural subregion volumes were seen in both the striatum and thalamus. There was a persistent pattern of increased volumes in girls with PS symptoms, but decreased volumes in boys with PS symptoms compared to non-PS youth in the bilateral posterior putamen of the striatum (F=9.26, pFDR=0.006), higher order thalamic bilateral pulvinar (F=9.85, pFDR=0.004), left medial dorsal nuclei (F=7.42, pFDR=0.01), as well as first order thalamic left ventral posterior nucleus (F=6.47, pFDR=0.02), medial geniculate nucleus (F=10.03, pFDR=0.004) and bilateral lateral geniculate nuclei (F=5.7, pFDR=0.03). However, both PS girls and boys had increased nucleus accumbens volumes (t=2.66, pFDR=0.02). Decreased functional connectivity was found in PS youth between a striatal subregion in the right posterior putamen (corresponding to the dorsal attention network) and occipital areas (pFWE=0.005). This pattern was found to be driven by differences in specifically PS boys and not PS girls (pFWE=0.004).DiscussionMultiple sex-specific structural differences between PS and non-PS youth were found in striatal and thalamic subregions. Hypo-connectivity between the striatal posterior putamen and occipital regions in PS boys overlap with structural increases in this subcortical volume in PS boys. Finding these early indicators is a key strategy to provide insight into neural mechanisms underlying the development of psychosis with the aim to improve and better target treatments.

Diagnostic utility of staging abdominal computerized tomography and repeat endoscopy in detecting localization errors at initial endoscopy in colorectal cancer.

Colonoscopy has a reported localization error rate as high as 21% in detecting colorectal neoplasms. Preoperative repeat endoscopy has been shown to be protective against localization errors. There is a paucity of literature assessing the utility of staging computerized tomography (CT) and repeat endoscopy as diagnostic tools for detecting localization errors following initial endoscopy. The objective of this study is to determine the diagnostic characteristics of staging CT and repeat endoscopy in correcting localization errors at initial endoscopy. A retrospective cohort study was conducted at a large tertiary academic center between January 2006 and August 2014. All patients undergoing surgical resection for CRC were identified. Group comparisons were conducted between (1) patients that underwent only staging CT (staging CT group), and (2) patients that underwent staging CT and repeat endoscopy (repeat endoscopy group). The primary outcome was localization error correction rate for errors at initial endoscopy. 594 patients were identified, 196 (33.0%) in the repeat endoscopy group, and 398 (77.0%) patients in the staging CT group. Error rates for each modality were as follows: initial endoscopy 8.8% (95% CI 6.5-11.0), staging CT 9.3% (95% CI 6.5-11.0), and repeat endoscopy 2.6% (95% CI 0.3-4.7); p < 0.01. Repeat endoscopy was superior to staging CT in correcting localization errors for left-sided / rectal lesions (81.2% vs. 33.3%; p < 0.01), right-sided lesions (80.0% vs. 54.5%; p = 0.21), and overall lesions (80.8% vs. 42.3%; p < 0.01). Repeat endoscopy compared to staging CT demonstrated relative risk reduction of 66.7% (95% CI 22-86%), absolute risk reduction of 38.5% (95% CI 14.2-62.8%), and odds ratio of 0.18 (95% CI 0.05-0.61) for correcting errors at initial endoscopy. Repeat endoscopy in colorectal cancer is superior to staging CT as a diagnostic tool for correcting localization-based errors at initial endoscopy.

Comparing Speech and Keyboard Text Entry for Short Messages in Two Languages on Touchscreen Phones

With the ubiquity of mobile touchscreen devices like smartphones, two widely used text entry methods have emerged: small touch-based keyboards and speech recognition. Although speech recognition has been available on desktop computers for years, it has continued to improve at a rapid pace, and it is currently unknown how today's modern speech recognizers compare to state-of-the-art mobile touch keyboards, which also have improved considerably since their inception. To discover both methods' “upper-bound performance,” we evaluated them in English and Mandarin Chinese on an Apple iPhone 6 Plus in a laboratory setting. Our experiment was carried out using Baidu's Deep Speech 2, a deep learning-based speech recognition system, and the built-in Qwerty (English) or Pinyin (Mandarin) Apple iOS keyboards. We found that with speech recognition, the English input rate was 2.93 times faster (153 vs. 52 WPM), and the Mandarin Chinese input rate was 2.87 times faster (123 vs. 43 WPM) than the keyboard for short message transcription under laboratory conditions for both methods. Furthermore, although speech made fewer errors during entry (5.30% vs. 11.22% corrected error rate), it left slightly more errors in the final transcribed text (1.30% vs. 0.79% uncorrected error rate). Our results show that comparatively, under ideal conditions for both methods, upper-bound speech recognition performance has greatly improved compared to prior systems, and might see greater uptake in the future, although further study is required to quantify performance in non-laboratory settings for both methods.

An Empirical Evaluation of Error Correction Methods and Tools for Next Generation Sequencing Data

Next Generation Sequencing (NGS) technologies produce massive amount of low cost data that is very much useful in genomic study and research. However, data produced by NGS is affected by different errors such as substitutions, deletions or insertion. It is essential to differentiate between true biological variants and alterations occurred due to errors for accurate downstream analysis. Many types of methods and tools have been developed for NGS error correction. Some of these methods only correct substitutions errors whereas others correct multi types of data errors. In this article, a comprehensive evaluation of three types of methods (k-spectrum based, Multi- sequencing alignment and Hybrid based) is presented which are implemented and adopted by different tools. Experiments have been conducted to compare the performance based on runtime and error correction rate. Two different computing platforms have been used for the experiments to evaluate effectiveness of runtime and error correction rate. The mission and aim of this comparative evaluation is to provide recommendations for selection of suitable tools to cope with the specific needs of users and practitioners. It has been noticed that k-mer spectrum based methodology generated superior results as compared to other methods. Amongst all the tools being utilized, Racer has shown eminent performance in terms of error correction rate and execution time for both small as well as large data sets. In multisequence alignment based tools, Karect depicts excellent error correction rate whereas Coral shows better execution time for all data sets. In hybrid based tools, Jabba shows better error correction rate and execution time as compared to brownie. Computing platforms mostly affect execution time but have no general effect on error correction rate.

First-principles study of magnetic order in graphene nanoflakes as spin logic devices

Scale effect and topological frustration can form magnetic order in the finite graphene structures (graphene nanoflakes (GNFs)). In this paper, the GNFs that can generate large net electron spin or electron spin antiferromagnetic coupling between local regions of net electron spins are classified reasonably. Representative special GNF configurations are proposed to be effectively used as fundamental logic gate devices for ultra-fast high density spintronics, and theoretically investigated by the first-principles electron structure calculations based on spin-polarized density functional theory. The first-principles calculations are performed by utilizing all-electron numerical-orbital scheme in the M11-L form of meta-GGA exchange-correlation functional. The energy spectrum of singly occupied states and the isodensity surface of total spin distribution indicate evidently that spin-single-state electrons are localized on two sides of a representative double-triangle GNF and the spin polarizations of two GNF segments are in opposite directions, resulting in antiferromagnetic coupling, which is consistent with the results derived from the graph theory and Lieb theorem. The energy of antiferromagnetic spin-coupled state is 55 meV lower than that of ferromagnetic spin-coupled state, which is obviously higher than the thermodynamic threshold of the minimum energy dissipation at room temperature. The spin coupling energy of the double triangle GNF increases with the scaling of GNF dimension increasing. The magnetic coupling strength of the double triangle GNF with and without mirror symmetry approach to the maximum stable values of 50 meV and 200 meV respectively, which are remarkably higher that of quantum dots and transition metal atom systems. Due to the fact that the spin coupling strength of the GNF logic gate spin device can reach 200 meV, it can operate normally at ambient temperature with an error rate of 0.001 which can be easily improved by an error correction technique. The calculation results demonstrate that the proposed GNF logic gate can finely operate at ambient temperature with significantly low and correctable error rate. Recent experimental studies show that graphene nanodevices on a scale of only a few nanometers can be successfully fabricated by etching technique of electron beam and scanning probe. Furthermore, the properties of GNF spin logic devices are not sensitive to intrinsic defects. The triangular GNF with n carbon rings has only (n+2)2-3 carbon atoms, while it can endure n-1 internal defects, thus persisting in non-bond states and local magnetic moments. It is suggested that the full spin logic gate devices based on GNF can be realized by using the current advanced nano-processing technology.

Powerful Genetic Association Analysis for Common or Rare Variants with High-Dimensional Structured Traits.

Many genetic association studies collect a wide range of complex traits. As these traits may be correlated and share a common genetic mechanism, joint analysis can be statistically more powerful and biologically more meaningful. However, most existing tests for multiple traits cannot be used for high-dimensional and possibly structured traits, such as network-structured transcriptomic pathway expressions. To overcome potential limitations, in this article we propose the dual kernel-based association test (DKAT) for testing the association between multiple traits and multiple genetic variants, both common and rare. In DKAT, two individual kernels are used to describe the phenotypic and genotypic similarity, respectively, between pairwise subjects. Using kernels allows for capturing structure while accommodating dimensionality. Then, the association between traits and genetic variants is summarized by a coefficient which measures the association between two kernel matrices. Finally, DKAT evaluates the hypothesis of nonassociation with an analytical P-value calculation without any computationally expensive resampling procedures. By collapsing information in both traits and genetic variants using kernels, the proposed DKAT is shown to have a correct type-I error rate and higher power than other existing methods in both simulation studies and application to a study of genetic regulation of pathway gene expressions.

Research on Parameter and Industrial Design in Multiple Sound Source Position

How to improve the performance of multiple sound source localization method, this paper studies the parameter and process design in multiple sound source position. First, it divides a signal into several sub-frames, and it calculates signal variation; then it uses the clustering algorithm to divide the estimated values into several categories; finally it calculates the final sound source location estimation. Experimental results show that the positioning algorithm is effective. There are many parameters will affect the gunfire correct rate and error rate, the parameters and angle selection will affect sound source position estimation.

Choosing appropriate analysis methods for cluster randomised cross-over trials with a binary outcome.

In cluster randomised cross-over (CRXO) trials, clusters receive multiple treatments in a randomised sequence over time. In such trials, there is usual correlation between patients in the same cluster. In addition, within a cluster, patients in the same period may be more similar to each other than to patients in other periods. We demonstrate that it is necessary to account for these correlations in the analysis to obtain correct Type I error rates. We then use simulation to compare different methods of analysing a binary outcome from a two-period CRXO design. Our simulations demonstrated that hierarchical models without random effects for period-within-cluster, which do not account for any extra within-period correlation, performed poorly with greatly inflated Type I errors in many scenarios. In scenarios where extra within-period correlation was present, a hierarchical model with random effects for cluster and period-within-cluster only had correct Type I errors when there were large numbers of clusters; with small numbers of clusters, the error rate was inflated. We also found that generalised estimating equations did not give correct error rates in any scenarios considered. An unweighted cluster-level summary regression performed best overall, maintaining an error rate close to 5% for all scenarios, although it lost power when extra within-period correlation was present, especially for small numbers of clusters. Results from our simulation study show that it is important to model both levels of clustering in CRXO trials, and that any extra within-period correlation should be accounted for. Copyright © 2016 John Wiley & Sons, Ltd.

Streaming High-Quality Mobile Video with Multipath TCP in Heterogeneous Wireless Networks

The proliferating wireless infrastructures with complementary characteristics prompt the bandwidth aggregation for concurrent video transmission in heterogeneous access networks. Multipath TCP (MPTCP) is an important transport-layer protocol recommended by IETF to integrate different access medium (e.g., Cellular and Wi-Fi). This paper investigates the problem of mobile video delivery using MPTCP in heterogeneous wireless networks with multihomed terminals. To achieve the optimal quality of real-time video streaming, we have to seriously consider the path asymmetry in different access networks and the disadvantages of the data retransmission mechanism in MPTCP. Motivated by addressing these critical issues, this study presents a novel quAlity-Driven MultIpath TCP (ADMIT) scheme that integrates the utility maximization based Forward Error Correction (FEC) coding and rate allocation. We develop an analytical framework to model the MPTCP-based video delivery quality over multiple communication paths. ADMIT is able to effectively integrate the most reliable access networks with FEC coding to minimize the end-to-end video distortion. The performance of ADMIT is evaluated through extensive semi-physical emulations in Exata involving H.264 video streaming. Experimental results show that ADMIT outperforms the reference transport protocols in terms of video PSNR (Peak Signal-to-Noise Ratio), end-to-end delay, and goodput. Thus, we recommend ADMIT for streaming high-quality mobile video in heterogeneous wireless networks with multihomed terminals.

FPGA Implementation of High Speed Error Detection and Correction of Orthogonal Codes using Segmentation Method

Background: Our main objective is to improve the error detection and correction capability using orthogonal codes with high security and speed. Statistical Analysis: In order to achieve high speed and security for error detection and correction, we have used cryptography technique. The concept of segmentation is specifically used, as it gives highly secured signal and also reduces the time complexity. Previous study incorporates mapping technique for error detection and correction. Our proposed methodology uses two decoders in place of mapping at the receiver end. This eases the performance and decreases the clock pulses. Findings: The proposed technique will send the k-bit data to encoders and it gets converted into orthogonal codes. The data is then encrypted using encryptor which consists of LFSR. The data is then sent to the receiver and then original data is retrieved using the decoders at the receiver. The multiple bit error correction can be done up to (n/4-1) bits. Here we have compared the delays for 4-bit, 5-bit, 6-bit, 7-bit, 8-bit data. After comparing our technique with the previous study we have found out that the delay time is gradually reduced. Our proposed work is done in n/2+1 comparison, where n represents the bit length of orthogonal codes. Hence this technique achieves 100% multiple bit error detection and error correction rate in the received signal. This technique is simulated in Xilinx software and implement using Field Programmable Gate Array (FPGA). Application/Improvements: This technique can be used for efficient transmission of data in the networks. There is also a wide scope for improvement to limit the bandwidth.

Cointegration, error correction and exchange rate forecasting

The finding that error correction models do not forecast better than the corresponding first difference models has been explained predominantly in terms of errors in the estimation of the adjustment mechanism. We argue against the proposition that the restrictions implied by economic theory must be imposed to enhance forecasting power because the underlying theory may not be necessarily valid. We put forward the proposition that an ECM and the corresponding first difference model have similar dynamic structures, which means that the relative performance of the two models is an empirical issue. The results do support this proposition and have some practical implications.

Atypical Amygdala Response to Fear Conditioning in Autism Spectrum Disorder

BackgroundAnxiety is the most common comorbid psychiatric concern in individuals diagnosed with autism spectrum disorder (ASD) and can cause significant functional impairment. Fear conditioning tasks offer a useful neurodevelopmental model for anxiety, yet there are no published neuroimaging studies of fear conditioning using ASD samples. MethodsTwenty adults diagnosed with ASD and 19 healthy adult control subjects completed a standard fear conditioning and extinction paradigm while undergoing functional magnetic resonance imaging scanning. A burst of air on the base of the neck was the unconditioned stimulus. Participants returned 1 day later for scanning during an extinction recall phase. A priori regions of interest were analyzed with a familywise error correction rate < 0.05. ResultsAll regions of interest demonstrated significantly greater response to threat than safe conditions during initial fear acquisition. Compared with age-matched control subjects, the ASD group showed a significantly decreased differential response to threat versus safe cues in right amygdala during the initial fear acquisition phase and decreased response in left amygdala during the first run of extinction recall on the second day of scanning. ConclusionsSymptoms of severe anxiety in ASD may arise from atypical neural mechanisms especially related to the differentiation of threat versus safe cues. An inability to effectively identify safety contexts may underlie chronically increased levels of anxiety in many individuals diagnosed with ASD.

A Combined Spell Checking and Error Correcting System for Punjabi -Hindi Language using Hybrid Approach

Spellchecker is software that analyzes possible misspellings in the text. It is the process of detecting and sometimes providing some suggestions for incorrectly spelled words in a text. If dictionary of spell checker is larger than higher is the error detection and error correction rate. Though considerable work has been done in English language but not much work has been done in regional languages of India including Punjabi and Hindi. Punjabi is the official language of Punjab state in India Punjabi is world’s 12th most widely spoken language In Punjabi language, there is very small amount of work is completed in this region.. Hindi is the official language of 11 vowels and 33 consonants. Hindi is also the third most spoken language in the world. A few work is done in Punjabi-Hindi spell detection and correction field and it is not easy task to identify errors in Punjabi-Hindi text. The spell checker systems are online available but as not stand-alone applications. The only available spell checker for Punjabi is “Akhar” ”Raftaar” and “Sudhaar”. “Akhar” is paid software that is not available free for its use to everybody and “Sudhaar” spell checker is a desktop application Some paid Hindi spell checker software’s are also online available. “Hinspell” & “Hinkhoj” are available spell checkers for Hindi language but a lot of improvement is needed. NLP (Natural language processing) is a field of computer science concerned with interaction between computer and human language. We have developed a combined spell checker and error correcting system for both Punjabi and Hindi Language. We used hybrid approach to implement the Spelling checking and Correcting System. The proposed system use hybrid approach which s a combination of various approaches like rule based approach, dictionary lookup approach , edit distance approach and NGram approach. Proposed system is tested with various inputs collected from different sources and results are found very accurate than that of existing system. Keywords: Spell Checking, Error Detection, Error Correction, Punjabi, Hindi, Dictionary-lookup, Hybrid approach.

Image Data Compression and Noisy Channel Error Correction Using Deep Neural Network

Everyday an enormous amount of information is stored, processed and transmitted digitally around the world. Neural Networks have been rapidly developed and researched as a solution to image processing tasks and channel error correction control. This paper presents a deep neural network (DNN) for gray image compression and a fault-tolerant transmission system with channel error-correction capabilities. First, a DNN implemented with the Levenberg-Marguardt learning algorithm is proposed for image compression. We demonstrate experimentally that our DNN not only provides better quality reconstructed images but also less computational capacity compared to DCT Zonal coding, DCT Threshold coding, Set Partitioning in Hierarchical Trees (SPIHT) and Gaussian Pyramid. Secondly, a DNN with improved channel error-correction rate is proposed. The experimental results indicate that our implemented network provides a superior error-correction ability by transmitting binary images over the noisy channel using Hamming and Repeat-Accumulate coding. Meanwhile, the network's storage requirement is 64 times less than the Hamming coding and 62 times less than the Repeat-Accumulate coding.

Evaluation of Second-Level Inference in fMRI Analysis.

We investigate the impact of decisions in the second-level (i.e., over subjects) inferential process in functional magnetic resonance imaging on (1) the balance between false positives and false negatives and on (2) the data-analytical stability, both proxies for the reproducibility of results. Second-level analysis based on a mass univariate approach typically consists of 3 phases. First, one proceeds via a general linear model for a test image that consists of pooled information from different subjects. We evaluate models that take into account first-level (within-subjects) variability and models that do not take into account this variability. Second, one proceeds via inference based on parametrical assumptions or via permutation-based inference. Third, we evaluate 3 commonly used procedures to address the multiple testing problem: familywise error rate correction, False Discovery Rate (FDR) correction, and a two-step procedure with minimal cluster size. Based on a simulation study and real data we find that the two-step procedure with minimal cluster size results in most stable results, followed by the familywise error rate correction. The FDR results in most variable results, for both permutation-based inference and parametrical inference. Modeling the subject-specific variability yields a better balance between false positives and false negatives when using parametric inference.

Powerful Tests for Multi-Marker Association Analysis Using Ensemble Learning.

Multi-marker approaches have received a lot of attention recently in genome wide association studies and can enhance power to detect new associations under certain conditions. Gene-, gene-set- and pathway-based association tests are increasingly being viewed as useful supplements to the more widely used single marker association analysis which have successfully uncovered numerous disease variants. A major drawback of single-marker based methods is that they do not look at the joint effects of multiple genetic variants which individually may have weak or moderate signals. Here, we describe novel tests for multi-marker association analyses that are based on phenotype predictions obtained from machine learning algorithms. Instead of assuming a linear or logistic regression model, we propose the use of ensembles of diverse machine learning algorithms for prediction. We show that phenotype predictions obtained from ensemble learning algorithms provide a new framework for multi-marker association analysis. They can be used for constructing tests for the joint association of multiple variants, adjusting for covariates and testing for the presence of interactions. To demonstrate the power and utility of this new approach, we first apply our method to simulated SNP datasets. We show that the proposed method has the correct Type-1 error rates and can be considerably more powerful than alternative approaches in some situations. Then, we apply our method to previously studied asthma-related genes in 2 independent asthma cohorts to conduct association tests.

Application of "Four Inspections and Eight Verifications" in preventing intravenous medical mistakes

Objective To investigate the effect of Four Inspections and Eight Verifications on preventing clinical nursing errors. Methods On the basis of the analysis about the nursing errors from our department of nursing in 2009, based on the request of the ten safety goal, Four Inspections and Eight Verifications was set up and carried out. Four Inspections and Eight Verifications was executed into practice in clinical care operations,into which one Inspection and Verification was added on the basis of Inspections and Seven Verifications. That was, the patients or their families participating to the survey, drug quality and validity are added. The rate of error occurrence and error correction were observed. Results Compared to the control group of Inspections and Seven Verifications, the nursing error rate dropped from 0.23%to 0.13% (χ2=7.89,P<0.01),the error rate of drug release decreased from 0.05%to 0.12%(χ2=8.29,P< 0.01),the error rate of correction increased from 0.01%to 0.04%(χ2=4.24,P<0.05). There was statistical significance between these two groups, which could guarantee the further implementation of the nursing safety measures. Conclusions It has superiority and feasibility, and is worthy of wide application compared with Three Inspections and Seven Verifications. Key words: Four Inspections and Eight Verifications; Prevention; Nursing error

Erreurs médicamenteuses induites par l’informatisation de la prescription à l’hôpital : recueil et analyse sur une période de 4 ans

Computerized physician order entry (CPOE) can generate medication errors. It is necessary to identify them and analyse their causes in order to secure the medication use system. Errors were recorded during the pharmaceutical analysis of prescriptions over a period of 4 years on 425 beds. A code frame was provided. Errors were classified according to type, causes and time of detection. The most often drug implicated and the error correction rate were studied. Deep causes were determined and contributing factors were listed. Among 99,536 prescriptions analyzed, 2636 errors were detected (2.65 errors per 100 orders analyzed). The most common error was omission (31.49%). The most represented cause was redundancy requirement (11.34%). Antibacterials were most commonly involved (224 errors). Exactly 65.9% of the prescriptions were modified by physicians. Three root causes were identified: (1) configuration issues; (2) misuse; (3) design problem. Three types of contributing factors have also been detailed: economic, human and technical factors. Identifying root causes has targeted three types of improvement actions: (1) software settings; (2) training of users; (3) requests for improvements. Contributing factors have to be identified to control the generated risk. Some errors related to CPOE may lead to serious side effects for the patient. That is why it is necessary to identify these errors and analyze them in order to implement improvement actions and prevention to secure the prescription.