Reduce Identification Errors Research Articles

Challenges in Lipidomics Biomarker Identification: Avoiding the Pitfalls and Improving Reproducibility.

Identification of features with high levels of confidence in liquid chromatography-mass spectrometry (LC-MS) lipidomics research is an essential part of biomarker discovery, but existing software platforms can give inconsistent results, even from identical spectral data. This poses a clear challenge for reproducibility in biomarker identification. In this work, we illustrate the reproducibility gap for two open-access lipidomics platforms, MS DIAL and Lipostar, finding just 14.0% identification agreement when analyzing identical LC-MS spectra using default settings. Whilst the software platforms performed more consistently using fragmentation data, agreement was still only 36.1% for MS2 spectra. This highlights the critical importance of validation across positive and negative LC-MS modes, as well as the manual curation of spectra and lipidomics software outputs, in order to reduce identification errors caused by closely related lipids and co-elution issues. This curation process can be supplemented by data-driven outlier detection in assessing spectral outputs, which is demonstrated here using a novel machine learning approach based on support vector machine regression combined with leave-one-out cross-validation. These steps are essential to reduce the frequency of false positive identifications and close the reproducibility gap, including between software platforms, which, for downstream users such as bioinformaticians and clinicians, can be an underappreciated source of biomarker identification errors.

Investigating the Professional Commitment and its Correlation with Patient Safety Culture and Patient Identification Errors: Evidence from a Cross-Sectional Study from Nurses' Perspectives

Introduction Nurses' professional commitment is crucial to their qualifications, impacting patient safety. This study aims to explore the relationship between nurses' professional commitment and patient safety culture, specifically focusing on patient identification errors. Methods A cross-sectional study was conducted in 2022 involving 340 nurses working in educational medical hospitals in southern Iran. Data collection instruments included questionnaires on nurses' professional commitment, patient identification errors, and patient safety culture. Descriptive and inferential statistics, such as t-tests, ANOVA, Pearson's correlation coefficient, and multiple linear regression, were performed using SPSS software version 23, with a significance level set at p = 0.05. Results The mean scores for professional commitment, patient safety culture, and errors in patient identification, as perceived by the nurses, were 77.64 ± 14.32 (out of 130), 2.71 ± 0.78 (out of 5), and 16.41 ± 4.58 (out of 32), respectively. There was a statistically significant correlation between professional commitment and errors in patient identification (r = -0.577, p < 0.001) as well as patient safety culture (r = 0.456, p < 0.001). Regression analysis revealed that nursing job satisfaction, understanding of nursing, self-sacrifice for the nursing profession, and engagement with the nursing profession were predictors of patient safety culture and errors in patient identification (p < 0.05). Conclusion Nurses' professional commitment was found to be at a moderate level. Furthermore, the dimensions of professional commitment significantly influenced patient safety culture and errors in patient identification. To enhance nurses' professional commitment, and subsequently improve patient safety culture and reduce identification errors, it is recommended to implement management mechanisms that increase job satisfaction, understanding, engagement, and professional self-sacrifice among nurses.

Research on loose bolt localization technology for transmission towers

Research on loose bolt fault in transmission towers is difficult to detect, and the localization of the loose bolts has always been a challenge in the field of structural health monitoring. This article investigates the relationship between the modal frequencies and modal shapes at different levels and finds that when bolts in transmission towers become loose, the order of modal frequency changes is consistent with the order of the mode shape at the location of the loosening. Based on this, a new method for locating loose bolts in transmission towers is proposed. In the identification of mode parameters, an improved variational mode decomposition is used to extract the mode components at different orders. A modal frequency identification method based on feature set signal reconstruction is proposed, which is based on the modal correlation between the signal components from different sensors, effectively preserving and extracting the structural modal characteristics from the vibration response. At the same time, the identification of modal shapes in transmission towers is achieved through feature-level fusion based on adaptive multi-sequence signals, greatly reducing identification errors. The effectiveness of this method is validated in the finite element analysis of transmission towers, and its superiority is demonstrated through comparison with more advanced methods. The results of a 110 kV transmission tower bolt loosening test also show that this method can accurately identify the area where the bolts are loose, with high identification accuracy, providing a new approach for the localization of loose bolts in transmission towers.

Research on Improving Denoising Performance of ROI Computer Vision Method for Transmission Tower Displacement Identification

The health monitoring technology of transmission towers based on vibration data had become a research hotspot. At present, vibration data mainly relied on sensors installed on the tower, which was time-consuming and laborious. Nevertheless, the ROI computer vision method could achieve long-distance, multi-point, and non-contact monitoring, which offers a new possibility for the structure-safety identification of power transmission towers. However, transmission towers are generally located in the field environment, and the background is complicated, resulting in the ROI key point method for vibration data acquisition encountering various types of noise. Thus, the key point in practice was clearing the noise and reducing the impact of noise on identification accuracy. The subpixel corner method was used to detect a minor error with the research object of pixel sets. The dilation + erosion method could reduce image noise. Under white noise with a variance of 0.05, the dilation + erosion could reduce average error (Emae) and mean square error (Emse) by 27% and 23% and increase percentages of data with absolute error less than 5 mm and 10 mm in the total number of data (σ5 and σ10) by 8% and 4.3%, respectively, which was compared to median filter + sharpen. The histogram equalization method was used to balance background lighting conditions and reduce identification errors from non-uniform illumination. Emae and Emse were reduced by 92% and 99%, and σ5 and σ10 were increased by 5 and 3 times, respectively, and the identification time was cut by 62% with the histogram equalization method. Under white noise with a variance of 0.15 or lower, the three methods combined increased the numerical stability of Emae, Emse, σ5, and σ10, which indicated that the combination of the three methods could improve the anti-noise performance, robustness, and identification accuracy of the ROI computer vision method for transmission tower displacement identification.

Reducing identification errors of African carnivores from photographs through computer-assisted workflow

Reducing identification errors of African carnivores from photographs through computer-assisted workflow

Classifier-adaptation knowledge distillation framework for relation extraction and event detection with imbalanced data

Fundamental information extraction tasks, such as relation extraction and event detection, suffer from a data imbalance problem. To alleviate this problem, existing methods rely mostly on well-designed loss functions to reduce the negative influence of imbalanced data. However, this approach requires additional hyper-parameters and limits scalability. Furthermore, these methods can only benefit specific tasks and do not provide a unified framework across relation extraction and event detection. In this paper, a Classifier-Adaptation Knowledge Distillation (CAKD) framework is proposed to address these issues, thus improving relation extraction and event detection performance. The first step is to exploit sentence-level identification information across relation extraction and event detection, which can reduce identification errors caused by the data imbalance problem without relying on additional hyper-parameters. Moreover, this sentence-level identification information is used by a teacher network to guide the baseline model’s training by sharing its classifier. Like an instructor, the classifier improves the baseline model’s ability to extract this sentence-level identification information from raw texts, thus benefiting overall performance. Experiments were conducted on both relation extraction and event detection using the Text Analysis Conference Relation Extraction Dataset (TACRED) and Automatic Content Extraction (ACE) 2005 English datasets, respectively. The results demonstrate the effectiveness of the proposed framework.

Benefits of lateral cephalogram during landmark identification on posteroanterior cephalograms.

ObjectivePrecise identification of landmarks on posteroanterior (PA) cephalograms is necessary when evaluating lateral problems such as facial asymmetry. The aim of the present study was to investigate whether the use of lateral (LA) cephalograms can reduce errors in landmark identification on PA cephalograms.MethodsFive examiners identified 16 landmarks (Cg, N, ANS, GT, Me, RO, Lo, FM, Z, Or, Zyg, Cd, NC, Ms, M, and Ag) on 32 PA cephalograms with and without LA cephalograms at the same time. The positions of the landmarks were recorded and saved in the horizontal and vertical direction. The mean errors and standard deviation of landmarks location according to the use of LA cephalograms were compared for each landmark.ResultsRelatively small errors were found for ANS, Me, Ms, and Ag, while relatively large errors were found for N, GT, Z, Or, and Cd. No significant difference was found between the horizontal and vertical errors for Z and Or, while large vertical errors were found for N, GT, and Cd. The value of identification error was lower when the landmarks were identified using LA cephalograms. Statistically significant error reductions were found at N and Cd with LA cephalograms, especially in the vertical direction.ConclusionsThe use of LA cephalograms during identification of landmarks on PA cephalograms could help reduce identification errors.

Camera-to-subject distance affects face configuration and perceived identity

Face identification is reliable for viewers who are familiar with the face, and unreliable for viewers who are not. One account of this contrast is that people become good at recognising a face by learning its configuration—the specific pattern of feature-to-feature measurements. In practice, these measurements differ across photos of the same face because objects appear more flat or convex depending on their distance from the camera. Here we connect this optical understanding to face configuration and identification accuracy. Changing camera-to-subject distance (0.32m versus 2.70m) impaired perceptual matching of unfamiliar faces, even though the images were presented at the same size. Familiar face matching was accurate across conditions. Reinstating valid distance cues mitigated the performance cost, suggesting that perceptual constancy compensates for distance-related changes in optical face shape. Acknowledging these distance effects could reduce identification errors in applied settings such as passport control.

Modelling confounding effects from extracerebral contamination and systemic factors on functional near-infrared spectroscopy

Haemodynamics-based neuroimaging is widely used to study brain function. Regional blood flow changes characteristic of neurovascular coupling provide an important marker of neuronal activation. However, changes in systemic physiological parameters such as blood pressure and concentration of CO2 can also affect regional blood flow and may confound haemodynamics-based neuroimaging. Measurements with functional near-infrared spectroscopy (fNIRS) may additionally be confounded by blood flow and oxygenation changes in extracerebral tissue layers. Here we investigate these confounds using an extended version of an existing computational model of cerebral physiology, ‘BrainSignals’. Our results show that confounding from systemic physiological factors is able to produce misleading haemodynamic responses in both positive and negative directions. By applying the model to data from previous fNIRS studies, we demonstrate that such potentially deceptive responses can indeed occur in at least some experimental scenarios. It is therefore important to record the major potential confounders in the course of fNIRS experiments. Our model may then allow the observed behaviour to be attributed among the potential causes and hence reduce identification errors.

Biometrics' new identity--measuring more physical and biological traits: Research into the characteristics that are unique to an individual is addressing the need to correctly identify people in a variety of medical, social and security contexts.

There is no need to remember complex combinations of random numbers and characters. From now on, you are the password: your fingerprint, face, iris, gait or odour—any of your potentially unique attributes—can theoretically be used to identify you. This is the idea behind biometrics, which was once confined to the realm of spy movies and high‐security facilities, but is now increasingly common in everyday security checks at borders, for secure payments and logging in to mobile devices. Beyond security, though, biometrics technology is also driving and enabling other applications that include forensic science, data sharing over networks and reducing identification errors in hospitals. > Biometrics is essentially ready for mass application, as the success of the Indian Aadhaar programme demonstrates. While fingerprints and retina patterns are the most well‐known biometric identifiers, they are not the only characteristics that can be used for biometric identification. Physical traits such as the shape of the face, hand or ear, the vasculature in the finger or DNA—so‐called hard biometrics—as well as behavioural characteristics such as gait, signature, voice and typing patterns can all be used to identify individuals (Fig 1). Another large and rapidly expanding category—which includes characteristics such as skin or eye colour, height, weight and tattoos—is known as “soft biometrics” and provides less distinctive, less categorical information, but has the advantage that such traits can be measured from a distance without cooperation from the subject. Even body odour is being actively explored as a practicable biometric identifier (http://www.sciencedaily.com/releases/2014/02/140204073823.htm). Figure 1. Examples of biometric traits. (A) Fingerprints, palm prints, hand vasculature, hand shape and signature. (B) Face, DNA, sclera (on the eyeball), ear shape and typing patterns (keystroke dynamics). (C) Teeth (forensic odontology), gait, voice or speech, iris and retina. Some of these traits—fingerprints, palm prints, face, voice, teeth, ear shape and DNA—are also used in forensics. Reproduced with permission …

Neutron activation analysis with a Monte Carlo simulation for kidney stones

Mechanistic questions regarding kidney stone formation have led researchers to look for the presence of trace elements. Neutron activation analysis is able to identify elements at parts-per-million concentrations. Four different types of kidney stones were irradiated with thermal neutrons to produce radioisotopes. Gamma spectroscopy of samples at different counting times was used to reduce identification errors by correlating results with the half-life of identified elements. For more precise identification, Monte Carlo simulation was used to cross-check the identification process. The simulation showed promising results that could lead to fast and accurate identification of trace elements as the simulation code is improved. Sodium (Na), potassium (K), calcium (Ca), bromine (Br), samarium (Sm), zinc (Zn), cadmium (Cd), ytterbium (Yb), gold (Au), cobalt (Co), and manganese (Mn) were identified as being present in the stones, by both the experimentally measured gamma spectrum and the simulation. Among these, Ca, Br, and Zn were found to be of potential clinical relevance via a literature review. Concentrations of the elements were compared to those noted in the literature. For uric acid stones, a correlation with the literature was found for Zn and Ca. A negative correlation was found between Zn and Br for non-uric acid stones. More samples are needed to test for statistical significance.

Optimal pose selection for calibration of planar anthropomorphic manipulators

The paper is devoted to the calibration experiment design for serial anthropomorphic manipulators with arbitrary number of links. It proposes simple rules for the selection of manipulator configurations that allow the user to essentially improve calibration accuracy and reduce identification errors. Although the main results have been obtained for the planar manipulators, they can be also useful for calibration of more complicated mechanisms. The efficiency of the proposed approach is illustrated with several examples that deal with typical planar manipulators and an anthropomorphic industrial robot.

Preliminary results from a crowdsourcing experiment in immunohistochemistry.

BackgroundCrowdsourcing, i.e., the outsourcing of tasks typically performed by a few experts to a large crowd as an open call, has been shown to be reasonably effective in many cases, like Wikipedia, the Chess match of Kasparov against the world in 1999, and several others. The aim of the present paper is to describe the setup of an experimentation of crowdsourcing techniques applied to the quantification of immunohistochemistry.MethodsFourteen Images from MIB1-stained breast specimens were first manually counted by a pathologist, then submitted to a crowdsourcing platform through a specifically developed application. 10 positivity evaluations for each image have been collected and summarized using their median. The positivity values have been then compared to the gold standard provided by the pathologist by means of Spearman correlation.ResultsContributors were in total 28, and evaluated 4.64 images each on average. Spearman correlation between gold and crowdsourced positivity percentages is 0.946 (p < 0.001).ConclusionsAim of the experiment was to understand how to use crowdsourcing for an image analysis task that is currently time-consuming when done by human experts. Crowdsourced work can be used in various ways, in particular statistically agregating data to reduce identification errors. However, in this preliminary experimentation we just considered the most basic indicator, that is the median positivity percentage, which provided overall good results. This method might be more aimed to research than routine: when a large number of images are in need of ad-hoc evaluation, crowdsourcing may represent a quick answer to the need.

Predicting and interpreting identification errors in military vehicle training using multidimensional scaling

We compared methods for predicting and understanding the source of confusion errors during military vehicle identification training. Participants completed training to identify main battle tanks. They also completed card-sorting and similarity-rating tasks to express their mental representation of resemblance across the set of training items. We expected participants to selectively attend to a subset of vehicle features during these tasks, and we hypothesised that we could predict identification confusion errors based on the outcomes of the card-sort and similarity-rating tasks. Based on card-sorting results, we were able to predict about 45% of observed identification confusions. Based on multidimensional scaling of the similarity-rating data, we could predict more than 80% of identification confusions. These methods also enabled us to infer the dimensions receiving significant attention from each participant. This understanding of mental representation may be crucial in creating personalised training that directs attention to features that are critical for accurate identification.Practitioner Summary: Participants completed military vehicle identification training and testing, along with card-sorting and similarity-rating tasks. The data enabled us to predict up to 84% of identification confusion errors and to understand the mental representation underlying these errors. These methods have potential to improve training and reduce identification errors leading to fratricide.

Reducing patient identification errors related to glucose point-of-care testing

Background: Patient identification (ID) errors in point-of-care testing (POCT) can cause test results to be transferred to the wrong patient’s chart or prevent results from being transmitted and reported. Despite the implementation of patient barcoding and ongoing operator training at our institution, patient ID errors still occur with glucose POCT. The aim of this study was to develop a solution to reduce identification errors with POCT. Materials and Methods: Glucose POCT was performed by approximately 2,400 clinical operators throughout our health system. Patients are identified by scanning in wristband barcodes or by manual data entry using portable glucose meters. Meters are docked to upload data to a database server which then transmits data to any medical record matching the financial number of the test result. With a new model, meters connect to an interface manager where the patient ID (a nine-digit account number) is checked against patient registration data from admission, discharge, and transfer (ADT) feeds and only matched results are transferred to the patient’s electronic medical record. With the new process, the patient ID is checked prior to testing, and testing is prevented until ID errors are resolved. Results: When averaged over a period of a month, ID errors were reduced to 3 errors/month (0.015%) in comparison with 61.5 errors/month (0.319%) before implementing the new meters. Conclusion: Patient ID errors may occur with glucose POCT despite patient barcoding. The verification of patient identification should ideally take place at the bedside before testing occurs so that the errors can be addressed in real time. The introduction of an ADT feed directly to glucose meters reduced patient ID errors in POCT.

Computer-Assisted Bar-Coding System Significantly Reduces Clinical Laboratory Specimen Identification Errors in a Pediatric Oncology Hospital

To assess the ability of a bar code-based electronic positive patient and specimen identification (EPPID) system to reduce identification errors in a pediatric hospital's clinical laboratory. An EPPID system was implemented at a pediatric oncology hospital to reduce errors in patient and laboratory specimen identification. The EPPID system included bar-code identifiers and handheld personal digital assistants supporting real-time order verification. System efficacy was measured in 3 consecutive 12-month time frames, corresponding to periods before, during, and immediately after full EPPID implementation. A significant reduction in the median percentage of mislabeled specimens was observed in the 3-year study period. A decline from 0.03% to 0.005% (P < .001) was observed in the 12 months after full system implementation. On the basis of the pre-intervention detected error rate, it was estimated that EPPID prevented at least 62 mislabeling events during its first year of operation. EPPID decreased the rate of misidentification of clinical laboratory samples. The diminution of errors observed in this study provides support for the development of national guidelines for the use of bar coding for laboratory specimens, paralleling recent recommendations for medication administration.

SU-FF-T-139: Designing a Patient Identification System Using Bioinformatics - A Fingerprint Approach

Purpose: We developed a computerized patient identification system employing fingerprint scanners and pattern recognition software to reduce identification errors in radiation therapy. Our customized system was developed to interact with the IMPAC record and verification system. Methods and Materials: The patient identification system was programmed with C++ language and equipped with an optical fingerprint scanner SDK (Suprema Inc®) to accept a scan, recognize the patient, and open the correct patient record in the record and verify system. A new patient initially has two fingers scanned (about 1 second each) and is photographed with web camera. During actual use, the patient has one finger scanned, the print is identified, (about 1 sec / 1000 records to search), a second finger is scanned for a second verification, and then the patient photograph is displayed. The error probability is one out of 1 billion. Once a patient is identified, our system calls IMPAC and opens the patient's file. We recruited 10 volunteers to create 50 virtual patients using 5 pairs of fingerprints per volunteer. This yields a database with 100 fingerprints for accuracy testing. Since the relative size of fingers on a hand varies, pairs of corresponding digits (e.g. right and left index fingers) from each volunteer were used as a virtual patient. This provided a test of scanning viability between digits (e.g. thumb vs. pinkie). A total of 115 tests were performed, each pair of fingers were tested 23 times. Results: We had no false positive results for identifying patients out of 111 accepted scans. The smallest finger failed to scan four times and was judged inadequate for identification. Conclusion: Optically scanned fingerprints can be used to accurately identify patients and open their IMPAC patient file. This system has great promise to ensure proper patient identification with minimal costs and effort.

Sources of Barcode Scanner Failure on POCT Devices

Abstract Barcodes offer a means of reducing identification errors from manual data entry. However, during implementation of barcodes in our intensive care unit, staff continued to use manual data entry due to the failure of the scanners to read the barcoded operator identification (ID) and patient bands on the first attempt. This study investigated the sources of barcode scanner failure. Two point-of-care testing (POCT) devices were examined: the i-Stat1 (East Windsor, NJ) and the Abbott PCx glucose meter (Bedford, MA). Patient bands and operator IDs used the interleaved 2 of 5 barcode symbology. For this trial, patient bands were placed on a staff member and his teenage daughter for 11 days (ankle and wrist) and 9 days (ankle) respectively (about twice the average institutional length of patient stay [4.9 days]). These bands were worn during normal activity, exercise, and showers. The i-Stat1 was less successful at scanning the 3 bands (9.1–47.5% failure rate, n = 22–71 attempts) than the PCx meter (2.9–30.8% failure rate, n = 20–68 attempts; n = 4 operators), although this difference was only significant for 1 of the ankle bands between devices (P = 0.03) and for scanning the operator ID between operators (P = 0.04). To determine device differences better, the worn bands were compared with new bands at various distances, angles, lighting, and barcode orientations. Staff were significantly more successful at scanning the new bands and ID badges when compared with worn bands on both devices (P