False Positive Matches Research Articles

The contribution of facial recognition technology to wrongful arrests and trauma.

When a crime is captured on video, law enforcement agencies increasingly have used facial recognition technology (FRT) to generate suspects to investigate. However, there are increasing examples of people who have been wrongfully arrested based because of the inaccurate results returned from these artificial intelligence-assisted searches of facial databases, despite very low error rates in the accuracy of these systems. We discuss the reliability of the evidence provided by a match returned by FRT, propose a framework for identifying potential problems with the use of FRT in criminal investigations, and review the research on the general trauma that comes from justice system involvement, trauma that is compounded by wrongful arrest and conviction. We also provide an analysis of how database size affects the evidentiary value of the matches returned by FRT. Variables like facial database size, race of the culprit, and quality of the probe photo can increase the likelihood that FRT systems will return false positive matches. The use of FRT for developing suspects in criminal investigations is likely to exacerbate the already profound racial disparities in the outcomes produced by the criminal legal system and increase trauma experienced by those who are wrongfully arrested or convicted. We recommend extreme caution surrounding its use. In addition, we call for more research on the trauma associated with wrongful arrest, which is likely to occur with the current use of FRT. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Adaptive Quotient Filters

Filters trade off accuracy for space and occasionally return false positive matches with a bounded error. Numerous systems use filters in fast memory to avoid performing expensive I/Os to slow storage. A fundamental limitation in traditional filters is that they do not change their representation upon seeing a false positive match. Therefore, the maximum false positive rate is only guaranteed for a single query, not for an arbitrary set of queries. We can improve the filter's performance on a stream of queries, especially on a skewed distribution, if we can adapt after encountering false positives. Adaptive filters, such as telescoping quotient filters and adaptive cuckoo filters, update their representation upon detecting a false positive to avoid repeating the same error in the future. Adaptive filters require an auxiliary structure, typically much larger than the main filter and often residing on slow storage, to facilitate adaptation. However, existing adaptive filters are not practical and have not been adopted in real-world systems for two main reasons. First, they offer weak adaptivity guarantees, meaning that fixing a new false positive can cause a previously fixed false positive to come back. Secondly, the sub-optimal design of the auxiliary structure results in adaptivity overheads so substantial that they can actually diminish overall system performance compared to a traditional filter. In this paper, we design and implement the \sysname, the first practical adaptive filter with minimal adaptivity overhead and strong adaptivity guarantees, which means that the performance and false-positive guarantees continue to hold even for adversarial workloads. The \sysname is based on the state-of-the-art quotient filter design and preserves all the critical features of the quotient filter such as cache efficiency and mergeability. Furthermore, we employ a new auxiliary structure design which results in considerably low adaptivity overhead and makes the \sysname practical in real systems. We evaluate the \sysname by using it to filter queries to an on-disk B-tree database and find no negative impact on insert or query performance compared to traditional filters. Against adversarial workloads, the \sysname preserves system performance, whereas traditional filters incur 2× slowdown from adversaries representing as low as 1% of the workload. Finally, we show that on skewed query workloads, the \sysname can reduce the false-positive rate 100× using negligible (1/1000th of a bit per item) space overhead.

Leveraging Machine Learning to Combat Missingness and Error in Data

ObjectivePoor quality data confounds efforts to link clients across datasets. To combat this, we have trialed an approach which seeks to identify linkage candidates using associations with related services. We utilised a machine learning (ML) approach to query linkage candidates’ dataset associations and make predictions about whether candidates are likely to be a client of the service being integrated for linkage. ApproachUtilizing a K-nearest neighbors algorithm, we trained a model using 15 variables to predict whether a linkage candidate is a client of a family service dataset. We then evaluated the model's success, and modified its native performance to minimise false positive matches. Subsequently, we tested the validity of these predictions as linkage criteria by employing blocking strategies and linking the service dataset to our linkage spine. The trained model was then used to identify correct links against the spine. ResultsThe evaluation of the machine learning model yielded promising results, with high accuracy (88.5%) and precision (95.5%). Testing the predictions as linkage criteria resulted in highly accurate links ranging from 96.0% to 98.7% across different blocking strategies. Despite some records failing to establish any links to the spine, rates of false positive matches remained low (0.7% to 3.1%). ConclusionMissingness and inaccuracy in data remains a key problem for data linkage, and a robust approach is required to resolve complex linkage cases. However, these findings suggest that machine learning can present novel options for a toolbox of many approaches to link problem records to a linkage spine.

DNA sequences alignment method using sparse index on pan-genome graph.

The graph of sequences represents the genetic variations of pan-genome concisely and space-efficiently than multiple linear reference genome. In order to accelerate aligning reads to the graph, an index of graph-based reference genomes is used to obtain candidate locations. However, the potential combinatorial explosion of nodes on the sequence graph leads to increasing the index space and maximum memory usage of alignment process considerably, especially for large-scale datasets. For this, existing methods typically attempt to prune complex regions, or extend the length of seeds, which sacrifices the recall of alignment algorithm despite reducing space usage slightly. We present the Sparse-index of Graph (SIG) and alignment algorithm SIG-Aligner, capable of indexing and aligning at the lower memory cost. SIG builds the non-overlapping minimizers index inside nodes of sequence graph and SIG-Aligner filters out most of the false positive matches by the method based on the pigeonhole principle. Compared to Giraffe, the results of computational experiments show that SIG achieves a significant reduction in index memory space ranging from 50% to 75% for the human pan-genome graphs, while still preserving superior or comparable accuracy of alignment and the faster alignment time.

73 Use of Probabilistic Linkage to Create a Novel Database to Study the Care of Bronchiolitis in Pediatric Intensive Care Units (PICU)

OBJECTIVES/GOALS: Bronchiolitis is a major cause of PICU admission, yet identifying best practices is limited by the lack of existing databases containing the needed demographic and clinical variables. We used probabilistic linkage to join the Virtual Pediatric System (VPS) and Pediatric Health Information Systems (PHIS) databases to overcome this data barrier. METHODS/STUDY POPULATION: We performed a single site study joining VPS and PHIS data. These national databases contain clinical (VPS) and billing and resource use (PHIS) data. Limits on the use of patient identifiers (PI) for multi-center research, makes direct linkage techniques impossible. To demonstrate that probabilistic linkage can accurately link VPS and PHIS records, we obtained our single site VPS and PHIS records and linked them using probabilistic linkage without PI and compared this to a gold standard linkage created with PI. We also compared demographic features of linked and unlinked records to assess the ability of probabilistic linkage to create a representative sample. RESULTS/ANTICIPATED RESULTS: We obtained 920 VPS records of patients with bronchiolitis and linked 91% (839/920) to a PHIS record with 4 (0.5%) false-positive matches. Characteristics of linked and unlinked records are compared in Table 1. Comparison of probabilistically linked and unlinked records showed no difference in median age in years (0.7 [Interquartile range (IQR) 0.3-1.5] v 0.7 [IQR 0.2-1.5], p = 0.76), median number of complex chronic conditions (0 [IQR 0-1] v 0 [IQR 0-1], p = 0.16), median Pediatric Index of Mortality 3 severity of illness scores (-4.6 [IQR -4.7 – 4.4] v -4.6 [IQR -4.7 – 4.4], p = 0.44), median days of PICU stay (4 [IQR 3-6] v 4 [IQR 2-6], p = 0.36), proportion female ( 44% v 46%, p = 0.82), or proportion of patients intubated (28% v 24%, p = 0.41). DISCUSSION/SIGNIFICANCE: Probabilistic linkage creates an accurate combined VPS-PHIS database. Extending our methodology to join data from all 38 hospitals contributing to VPS and PHIS will allow creation of a large database containing the demographic, treatment, and outcome data needed to enable Comparative Effectiveness Research of bronchiolitis care.

Probabilistic Linkage Creates a Novel Database to Study Bronchiolitis Care in the PICU.

Lack of a comprehensive database containing diagnosis, patient and clinical characteristics, diagnostics, treatments, and outcomes limits needed comparative effectiveness research (CER) to improve care in the PICU. Combined, the Pediatric Hospital Information System (PHIS) and Virtual Pediatric Systems (VPS) databases contain the needed data for CER, but limits on the use of patient identifiers have thus far prevented linkage of these databases with traditional linkage methods. Focusing on the subgroup of patients with bronchiolitis, we aim to show that probabilistic linkage methods accurately link data from PHIS and VPS without the need for patient identifiers to create the database needed for CER. We used probabilistic linkage to link PHIS and VPS records for patients admitted to a tertiary children's hospital between July 1, 2017 to June 30, 2019. We calculated the percentage of matched records, rate of false-positive matches, and compared demographics between matched and unmatched subjects with bronchiolitis. We linked 839 of 920 (91%) records with 4 (0.5%) false-positive matches. We found no differences in age (P = .76), presence of comorbidities (P = .16), admission illness severity (P = .44), intubation rate (P = .41), or PICU stay length (P = .36) between linked and unlinked subjects. Probabilistic linkage creates an accurate and representative combined VPS-PHIS database of patients with bronchiolitis. Our methods are scalable to join data from the 38 hospitals that jointly contribute to PHIS and VPS, creating a national database of diagnostics, treatment, outcome, and patient and clinical data to enable CER for bronchiolitis and other conditions cared for in the PICU.

Novel Anticounterfeiting Solution Based on 2D Materials Produced by Electrochemical Exfoliation.

This work demonstrates the use of 2D materials (2DMs) as identification tags by exploiting their unique shape. Electrochemical exfoliation enables the production of large quantities of optically accessible 2DMs with diverse morphology and large lateral sizes up to 20µm. Image processing techniques are used to facilitate shape identification and matching within a dataset of 500 unique nanosheets. Rotational and translation invariant shape matching with no false positive matches between over 100 000 unique shape pairings is shown. The approach enables individual nanosheets to be deposited onto products, such as packaging of luxury goods, pharmaceuticals, banknotes, etc., as a unique seal of authenticity. Quick inspection of the nanoscale tag by optical microscopy allows the shape to be compared against the genuine dataset, enabling unique identification. The optical features of 2D materials, such as Raman and/or photoluminescence signals can be used as an additional chemical fingerprint, making the anticounterfeiting solution very robust.

Distance-based linkage of personal microbiome records for identification and its privacy implications

Due to its high potential for analysis in clinical settings, research on the human microbiome has been flourishing for several years. As an increasing amount of data on the microbiome is gathered and stored, analysing the temporal and individual stability of microbiome readings, and the succeeding privacy risks, has gained importance. In 2015, Franzosa et al. demonstrated the feasibility of matching and linking individuals in microbiome-based datasets from the Human Microbiome Project, which could lead to re-identification of individuals, and thus poses privacy implications for microbiome study designs. Their technique is based on the construction of body site-specific metagenomic codes that maintain a certain stability over time.In this paper, we establish a distance-based technique for personal microbiome identification, which is combined with a solution for avoiding spurious, false positive matches. In a direct comparison with the approach from Franzosa et al., which assumes that information is available as microbial records, rather than at the more detailed (but less likely to be shared) nucleic acid level, our method improves upon the identification results on most of the considered datasets. Our main finding is an increase of the average percentage of true positive identifications of 30% on the widely studied microbiome of the gastrointestinal tract. While we particularly recommend our method for application on the gut microbiome, we also observed substantial identification success on other body sites. Our results demonstrate the potential of privacy threats in microbiome data gathering, storage, sharing, and analysis, and thus underline the need for solutions to protect the microbiome as personal and sensitive medical data. We also show that the method is robust to various hyper-parameter settings.Based on our observations, we further identify challenges in personal microbiome identification research, specifically, the scarcity of benchmark data and associated data analysis tasks. Based on our experience, we propose solutions for a more systematic and comparable evaluation, considering also aspects of costs entailed with applying privacy-preserving methods.

Beyond the Bound: A New Performance Perspective for Identification via Channels

Identification via channels (ID) is a goal-oriented (Post-Shannon) communications paradigm that verifies the matching of message (identity) pairs at source and sink. To date, ID research has focused on the upper bound λ for the probability of a false-positive (FP) identity match, mainly through ID tagging codes that represent the identities through ID codeword sets consisting of position-tag tuples. We broaden the ID research scope by introducing novel ID performance metrics: the expected FP-error probability <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fp</sub> which considers distance properties of ID codeword sets in conjunction with the probability for selecting ID pairs, the threshold probabilities <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ϵ</sub> that characterize quantiles of FP-probabilities, and the distance tail uplift ratio DiTUR giving the fraction of ID pairs whose distance is increased above the minimum distance (which corresponds to λ). We define a No-Code (NC) approach that directly conducts the ID operations with the messages (identities) without any additional coding as a baseline for ID. We investigate a concatenated Reed-Solomon ID code and a Reed-Muller ID code, and find that they do not always yield advantages over using no ID code. We analytically characterize the reduction of error-prone ID pairs through sending multiple tags. Overall, our insights point to investigating the distance distribution of ID codes and incorporating the ID pair distributions of real ID systems in future ID research.

Inference of partial colexifications from multilingual wordlists.

The past years have seen a drastic rise in studies devoted to the investigation of colexification patterns in individual languages families in particular and the languages of the world in specific. Specifically computational studies have profited from the fact that colexification as a scientific construct is easy to operationalize, enabling scholars to infer colexification patterns for large collections of cross-linguistic data. Studies devoted to partial colexifications-colexification patterns that do not involve entire words, but rather various parts of words-, however, have been rarely conducted so far. This is not surprising, since partial colexifications are less easy to deal with in computational approaches and may easily suffer from all kinds of noise resulting from false positive matches. In order to address this problem, this study proposes new approaches to the handling of partial colexifications by (1) proposing new models with which partial colexification patterns can be represented, (2) developing new efficient methods and workflows which help to infer various types of partial colexification patterns from multilingual wordlists, and (3) illustrating how inferred patterns of partial colexifications can be computationally analyzed and interactively visualized.

Copy Move Forgery Detection Using Novel Quadsort Moth Flame Light Gradient Boosting Machine

A severe problem in modern information systems is Digital media tampering along with fake information. Even though there is an enhancement in image development, image forgery, either by the photographer or via image manipulations, is also done in parallel. Numerous researches have been concentrated on how to identify such manipulated media or information manually along with automatically; thus conquering the complicated forgery methodologies with effortlessly obtainable technologically enhanced instruments. However, high complexity affects the developed methods. Presently, it is complicated to resolve the issue of the speed-accuracy trade-off. For tackling these challenges, this article put forward a quick and effective Copy-Move Forgery Detection (CMFD) system utilizing a novel Quad-sort Moth Flame (QMF) Light Gradient Boosting Machine (QMF-Light GBM). Utilizing Borel Transform (BT)-based Wiener Filter (BWF) and resizing, the input images are initially pre-processed by eliminating the noise in the proposed system. After that, by utilizing the Orientation Preserving Simple Linear Iterative Clustering (OPSLIC), the pre-processed images, partitioned into a number of grids, are segmented. Next, as of the segmented images, the significant features are extracted along with the feature’s distance is calculated and matched with the input images. Next, utilizing the Union Topological Measure of Pattern Diversity (UTMOPD) method, the false positive matches that took place throughout the matching process are eliminated. After that, utilizing the QMF-Light GBM visualization, the visualization of forged in conjunction with non-forged images is performed. The extensive experiments revealed that concerning detection accuracy, the proposed system could be extremely precise when contrasted to some top-notch approaches.

An atlas of the blood virome in healthy individuals

Emerging evidence indicates that gut virome plays a role in human health and disease, however, much less is known about the viral communities in blood. Here we conducted a direct metatranscriptomic sequencing of virus-like-particles in blood from 1200 healthy individuals, without prior amplification to avoid potential amplification bias and with a strictly bioinformatic and manual check for candidate viral reads to reduce false-positive matches. We identified 55 different viruses from 36 viral families, including 24 human DNA, RNA and retroviruses in 70% of the studied pools. The study showed that anelloviruses are widely distributed and dominate the blood virome in healthy individuals. Human herpesviruses and pegivirus-1 are commonly prevalent in asymptomatic humans. We identified the prevalence of RNA viruses often causing acute infection, like HEV, HPIV, RSV and HCoV-HKU1, revealing of a transmissible risk of asymptomatic infection. Several viruses possible related to transfusion safety were identified, including human Merkel cell polyomavirus, papillomavirus, parvovirus B19 and herpesvirus 8 in addition to HBV. In addition, phages in Caudovirales and Microviridae, were commonly found in pools of samples with a very low abundance; a few sequences for invertebrate, plant and giant viruses were found in some of individuals; however, the remaining 31 viruses mostly reflect extensive contamination from commercial reagents and the work environments. In conclusion, this study is the first comprehensive investigation of blood virome in healthy individuals by metatranscriptomic sequencing of VLP in China. Further investigation of potential false positives representing a major challenge for the identification of novel viruses in mNGS, will offer a systemic idea and means to reveal true viral infections of human.

Quantifying Privacy Risks for Continuous Trait Data

In the context of life sciences, the rapid biotechnical development leads to the creation of huge amounts of biological data. The use of such data naturally brings concerns on human genetic privacy breaches, which also discourage biological data sharing. Prior studies have investigated the possibility of the privacy issues associated with individuals’ trait data. However, there are few studies on quantitatively analyzing the probability of the privacy risk. In this paper, we fill this void by proposing a scheme for systematically breaching genomic privacy, which is centered on quantifying the probability of the privacy risk of continuous trait data. With well-designed synthetic datasets, our theoretical analysis and experiments lead to several important findings, such as: (i) The size of genetic signatures and the sensitivity (true positive rate) significantly affect the accuracy of re-identification attack. (ii) Both the size of genetic signatures and the minor allele frequency have a significant impact on distinguishing true positive and false positive matching between traits and genetic profiles. (iii) The size of the matching quantitative trait locus dataset has a large impact on the confidence of the privacy risk assessment. Validation with a real dataset shows that our findings can effectively estimate the privacy risks of the continuous trait dataset.

Pillin: A Family of Sword Cutlers

To many with an interest in British military swords the name of Pillin will be very familiar. Yet, for a firm which traded for at least 65 years there is little information available about its activities beyond a few bare dates and addresses often quoted from the same sources. The original source of much of this information is unclear and some of this now appears questionable. In writing this article my intention is to provide a more comprehensive account of the Pillin dynasty. By examining what new evidence may be available I hope to evaluate and revise contemporary wisdom regarding Pillin’s operations. In conducting this research during a global Covid-19 pandemic, amid national and local lockdowns, I am restricted in source material but have relied heavily on sources available online. Such methodology carries its own risks, there are gaps in the available datasets and frustratingly, transcription errors can both hamper searches and offer false positive matches. Nevertheless, with the rise in popularity of ancestry research, new data sources are constantly being made available. In today’s world of social media and online resources we have ready access to data of which researchers even 15 years ago could only have dreamed. Information regarding sword cutlers, such as their trading dates and addresses, is of particular interest as it can help to correctly date a sword. This in turn can assist with identifying an original owner. In the case of British blades, etched with the owner’s initials or a family crest, cross referencing with the Army lists will often provide multiple possible candidates over the lifetime of a sword pattern. Accurately identifying the year of manufacture can significantly assist in narrowing the field of potential matches against any one set of initials. A desire to identify the original owners of some of my own collected swords, and to assist others to do the same, provided the seed of motivation for my research into the Pillin dynasty. I hope this article provides a useful platform for additional research.

IMPROVING RANSAC FEATURE MATCHING BASED ON GEOMETRIC RELATION

Abstract. Feature Matching between images is an essential task for many computer vision and photogrammetry applications, such as Structure from Motion (SFM), Surface Extraction, Visual Simultaneous Localization and Mapping (VSLAM), and vision-based localization and navigation. Among the matched point pairs, there are typically false positive matches. Therefore, outlier detection and rejection are important steps in any vision application. RANSAC has been a well-established approach for outlier detection. The outlier ratio and the number of required correspondences used in RANSAC determine the number of iterations needed, which ultimately, determines the computation time. We propose a simple algorithm (GR_RANSAC) based on the two-dimensional spatial relationships between points in the image domain. The assumption is that the distances and bearing angles between the 2D feature points should be similar in images with small disparity, such as the case for video image sequences. In the proposed approach, the distances and angles are measured from a reference point in the first image and its correspondence in the other image, and the points with any significant differences are considered as outliers. This process can pre-filter the matched points, and thus increase the inliers’ ratio. As a result, GR_RANSAC can converge to the correct hypothesis in fewer trial runs than ordinary RANSAC.

A New Three-dimensional Integrated Multi-index Method for CBIR System

This paper proposes a new image retrieval method called the 3D integrated multi-index to fuse SIFT (Scale Invariant Feature Transform) visual words with other features at the indexing level. The advantage of the 3D integrated multi-index is that it can produce finer subdivisions in the search space. Compared with the inverted indices of medium-sized codebook, the proposed method increases time slightly in preprocessing and querying. Particularly, the SIFT, contour and colour features are fused into the integrated multi-index, and the joint cooperation of complementary features significantly reduces the impact of false positive matches, so that effective image retrieval can be achieved. Extensive experiments on five benchmark datasets show that the 3D integrated multi-index significantly improves the retrieval accuracy. While compared with other methods, it requires an acceptable memory usage and query time. Importantly, we show that the 3D integrated multi-index is well complementary to many prior techniques, which make our method compared favorably with the state-of-the-arts.

Learnings From Precision Clinical Trial Matching for Oncology Patients Who Received NGS Testing.

PURPOSETumor next-generation sequencing reports typically generate trial recommendations for patients based on their diagnosis and genomic profile. However, these require additional refinement and prescreening, which can add to physician burden. We wanted to use human prescreening efforts to efficiently refine these trial options and also elucidate the high-value parameters that have a major impact on efficient trial matching.METHODSClinical trial recommendations were generated based on diagnosis and biomarker criteria using an informatics platform and were further refined by manual prescreening. The refined results were then compared with the initial trial recommendations and the reasons for false-positive matches were evaluated.RESULTSManual prescreening significantly reduced the number of false positives from the informatics generated trial recommendations, as expected. We found that trial-specific criteria, especially recruiting status for individual trial arms, were a high value parameter and led to the largest number of automated false-positive matches.CONCLUSIONReflex clinical trial matching approaches that refine trial recommendations based on the clinical details as well as trial-specific criteria have the potential to help alleviate physician burden for selecting the most appropriate trial for their patient. Investing in publicly available resources that capture the recruiting status of a trial at the cohort or arm level would, therefore, allow us to make meaningful contributions to increase the clinical trial enrollments by eliminating false positives.

Matching Large Baseline Oblique Stereo Images Using an End-to-End Convolutional Neural Network

The available stereo matching algorithms produce large number of false positive matches or only produce a few true-positives across oblique stereo images with large baseline. This undesired result happens due to the complex perspective deformation and radiometric distortion across the images. To address this problem, we propose a novel affine invariant feature matching algorithm with subpixel accuracy based on an end-to-end convolutional neural network (CNN). In our method, we adopt and modify a Hessian affine network, which we refer to as IHesAffNet, to obtain affine invariant Hessian regions using deep learning framework. To improve the correlation between corresponding features, we introduce an empirical weighted loss function (EWLF) based on the negative samples using K nearest neighbors, and then generate deep learning-based descriptors with high discrimination that is realized with our multiple hard network structure (MTHardNets). Following this step, the conjugate features are produced by using the Euclidean distance ratio as the matching metric, and the accuracy of matches are optimized through the deep learning transform based least square matching (DLT-LSM). Finally, experiments on Large baseline oblique stereo images acquired by ground close-range and unmanned aerial vehicle (UAV) verify the effectiveness of the proposed approach, and comprehensive comparisons demonstrate that our matching algorithm outperforms the state-of-art methods in terms of accuracy, distribution and correct ratio. The main contributions of this article are: (i) our proposed MTHardNets can generate high quality descriptors; and (ii) the IHesAffNet can produce substantial affine invariant corresponding features with reliable transform parameters.

DLFT: Data and Layout Aware Fault Tolerance Framework for Big Data Transfer Systems

Various scientific research organizations generate several petabytes of data per year through computational science simulations. These data are often shared by geographically distributed data centers for data analysis. One of the major challenges in distributed environments is failure; hardware, network, and software might fail at any instant. Thus, high-speed and fault tolerant data transfer frameworks are vital for transferring such large data efficiently between the data centers. In this study, we proposed a bloom filter-based data aware probabilistic fault tolerance (DAFT) mechanism that can handle such failures. We also proposed a data and layout aware mechanism for fault tolerance (DLFT) to effectively handle the false positive matches of DAFT. We evaluated the data transfer and recovery time overheads of the proposed fault tolerance mechanisms on the overall data transfer performance. The experimental results demonstrated that the DAFT and DLFT mechanisms exhibit a maximum of 10% and a minimum of 2% recovery time overhead at 80% and 20% fault points respectively. However, we observed minimum to negligible overhead with respect to the overall data transfer rate.

Accuracy of an Electronic Health Record Patient Linkage Module Evaluated between Neighboring Academic Health Care Centers.

Patients often seek medical treatment among different health care organizations, which can lead to redundant tests and treatments. One electronic health record (EHR) platform, Epic Systems, uses a patient linkage tool called Care Everywhere (CE), to match patients across institutions. To the extent that such linkages accurately identify shared patients across organizations, they would hold potential for improving care. This study aimed to understand how accurate the CE tool with default settings is to identify identical patients between two neighboring academic health care systems in Southern California, The University of California Los Angeles (UCLA) and Cedars-Sinai Medical Center. We studied CE patient linkage queries received at UCLA from Cedars-Sinai between November 1, 2016, and April 30, 2017. We constructed datasets comprised of linkages ("successful" queries), as well as nonlinkages ("unsuccessful" queries) during this time period. To identify false positive linkages, we screened the "successful" linkages for potential errors and then manually reviewed all that screened positive. To identify false-negative linkages, we applied our own patient matching algorithm to the "unsuccessful" queries and then manually reviewed a sample to identify missed patient linkages. During the 6-month study period, Cedars-Sinai attempted to link 181,567 unique patient identities to records at UCLA. CE made 22,923 "successful" linkages and returned 158,644 "unsuccessful" queries among these patients. Manual review of the screened "successful" linkages between the two institutions determined there were no false positives. Manual review of a sample of the "unsuccessful" queries (n = 623), demonstrated an extrapolated false-negative rate of 2.97% (95% confidence interval [CI]: 1.6-4.4%). We found that CE provided very reliable patient matching across institutions. The system missed a few linkages, but the false-negative rate was low and there were no false-positive matches over 6 months of use between two nearby institutions.