Multiple Data Streams Research Articles

Quality Improvement through Intensive Care Registries: The Value of Big Data in Clinical Decision Making

Background: Quality of Intensive care has got more attention in case of the high cost of healthcare and the potential for harm. Poor-quality care causes high cost and quality improvement initiatives in the ICU lead to an improvement in outcomes as well as a decrease in costs. One of the crucial tools that allow physicians and nurses to monitor change in a quality improvement effort is the development of an electronic database for data collection and reporting. The objective of Intensive Care Registries is to create a high-quality registry of patients through a collaboration of academic health centers performing uniform data collection with the purpose of improving the quality and accuracy of healthcare decisions and provide a data-driven clinical decision support system for critical care medicine. Methods: This article reviews real-world data sources in healthcare and considers registry as the main tool to address health services and outcomes research questions in critical care, and briefly describes objective, inputs and outputs of intensive care registries. As it can be comprehended from library research, the combination of patient clinical care data, quality parameters, and ICU operating costs, integrated into an electronic database, provides a valuable tool for quality improvement and overall efficiency of offered care. Results: Using Big Data effectively within ICUs for supporting clinical decision making can lead to predict numerous diseases and help to discover new patterns in healthcare. The ability to process multiple high-speed clinical data streams from multiple centers could dramatically improve both healthcare efficiency and patient outcomes. Conclusion: To gain this goal, developing reliable and standardized health analytics platforms as well as quality improvement processes that translate analytical results into new clinical guidelines, is recommended.

Zero-Forcing-Based Downlink Virtual MIMO–NOMA Communications in IoT Networks

To support massive connectivity and boost spectral efficiency for Internet of Things (IoT), a downlink scheme combining virtual multiple-input–multiple-output (MIMO) and nonorthogonal multiple access (NOMA) is proposed. All the single-antenna IoT devices in each cluster cooperate with each other to establish a virtual MIMO entity, and multiple independent data streams are requested by each cluster. NOMA is employed to superimpose all the requested data streams, and each cluster leverages zero-forcing detection to demultiplex the input data streams. Only statistical channel state information (CSI) is available at the base station to avoid the waste of the energy and bandwidth on frequent CSI estimations. The outage probability and goodput of the virtual MIMO–NOMA system are thoroughly investigated by considering the Kronecker model, which embraces both the transmit and receive correlations. Furthermore, the asymptotic results facilitate not only the exploration of physical insights but also the goodput maximization. In particular, the asymptotic outage expressions provide quantitative impacts of various system parameters and enable the investigation of diversity–multiplexing tradeoff (DMT). Moreover, power allocation coefficients and/or transmission rates can be properly chosen to achieve the maximal goodput. By favor of the Karush–Kuhn–Tucker conditions, the goodput maximization problems can be solved in closed form, with which the joint power and rate selection is realized by using alternately iterating optimization. Besides, the optimization algorithms tend to allocate more power to clusters under unfavorable channel conditions and support clusters with a higher transmission rate under benign channel conditions.

Coherent Communications in Self-Organizing Networks With Distributed Beamforming

Coherent communications aim to support higher data rates and extended connectivity at lower power consumption compared with traditional point-to-point transmissions. The typical setting of coherent communication schemes is based on a single data stream with multiple transmitters and a single receiver. This paper studies the case of multiple concurrent data streams, each with multiple transmitters and multiple receivers, in self-organizing wireless networks. A distributed optimization solution based on joint network formation and beamforming is developed for coherent group communications in a network of nodes that need to communicate over long distances. This solution significantly improves the power gain for a single data stream and the signal-to-interference-ratio for multiple data streams compared to point-to-point communications. These gains are in turn translated to improvements in communication range, power efficiency, reliability, and throughput. In this multi-layer network optimization solution, nodes coordinate with each other in a distributed manner to form transmitter and receiver groups, and communicate with each other coherently over long distances. The coherent beamforming protocol is optimized for given transmitter and receiver groups, whereas the network formation protocol is optimized to determine these groups. By using single-antenna communication nodes, the proposed optimization solution provides a major gain in network communications and outperforms other benchmark combinations of beamforming and network formation protocols.

Theoretical Analysis of Various Software-Defined Multiplexing Codes

How to combine multiple data-streams for transmission in aggregate is a very interesting problem, especially for the emerging software-defined networks nowadays. The conventional packet-based protocols cannot provide the flexibility for combining data-streams in the ad hoc nature. If the number of data-streams changes over time, the existing packet formats cannot handle the transmission of multiple data-streams effectively. The effectiveness is measured by two performance metrics, namely coding efficiency and data-transmission intermittency. We propose a new software-defined multiplexing code (SDMC) approach, which can combine (multiplex) multiple data-streams easily and is much more effective than the conventional packet-based method. Three SDMC schemes (distributed, hierarchical, and hybrid) are compared theoretically and by simulation. A trade-off between these two performance metrics can be found when one selects one of the three SDMC schemes for combining multiple data-streams. The hierarchical SDMC scheme brings about the highest coding efficiency while the hybrid SDMC scheme suffers from the smallest overall intermittency.

Codebook-Based Precoding and Power Allocation for MU-MIMO Systems for Sum Rate Maximization

In this paper, we study the problem of downlink (DL) sum rate maximization in codebook based multiuser (MU) multiple input multiple output (MIMO) systems. The user equipments (UEs) estimate the DL channels using pilot symbols sent by the access point (AP) and feedback the estimates to the AP over a control channel. We present a closed form expression for the achievable sum rate of the MU-MIMO broadcast system with codebook constrained precoding based on the estimated channels, where multiple data streams are simultaneously transmitted to all users. Next, we present novel, computationally efficient, minorization-maximization (MM) based algorithms to determine the selection of beamforming vectors and power allocation to each beam that maximizes the achievable sum rate. Our solution involves multiple uses of MM in a nested fashion. Based on this approach, we propose and contrast two algorithms, which we call the square-root-MM (SMM) and inverse-MM (IMM) algorithms. The algorithms are iterative and converge to a locally optimal beamforming vector selection and power allocation solution from any initialization. We evaluate the performance and complexity of the algorithms for various values of the system parameters, compare them with existing solutions, and provide further insights into how they can be used in system design.

Multi-User Multi-Stream mmWave WLANs With Efficient Path Discovery and Beam Steering

Multi-stream 60 GHz communication can potentially achieve data rates up to 100 Gbps via multiplexing multiple data streams. Unfortunately, establishing multi-stream directional links is a high overhead procedure as the search space increases with the number of spatial streams and the product of AP-client beam resolution. In this paper, we present MUlti-stream beam-Training for mm-wavE networks (MUTE) a novel system that leverages channel sparsity, GHz-scale sampling rate, and the knowledge of mm-Wave RF codebook beam patterns to construct a set of candidate beams for efficient multi-stream beam steering. MUTE repurposes the mandatory periodic beam sweeps in 60 GHz WLANs to discover the dominant paths of the mmWave channel between the AP and any client with zero additional overhead. Coupling path estimates with beam pattern knowledge, MUTE selects a set of candidate beams that capture diverse or ideally orthogonal paths to obtain maximum stream separability. Our over-the-air experiments demonstrate that MUTE achieves 90% of the maximum achievable aggregate PHY rate while incurring only 1.2% of exhaustive search’s training overhead.

Receiver Algorithms for Single-Carrier OSM Based High-Rate Indoor Visible Light Communications

In intensity-modulation and direct-detection (IM/DD) multiple-input and multiple-output (MIMO) visible light communication (VLC) systems, spatial subchannels are usually correlated, and spatial modulation is a good choice to achieve the advantages of MIMO technology. Peak-to-average power ratio (PAPR) is a key issue in VLCs due to the limited linear dynamic range of light emitting diodes (LEDs). Single-carrier communication systems have a lower PAPR than orthogonal frequency division multiplexing (OFDM) communication systems. However, it is challenging to design a single-carrier spatial modulation for high-rate transmissions because of the time domain intersymbol interference. This paper develops an optical spatial modulation (OSM) scheme based on bipolar pulse amplitude modulation (PAM) and spatial elements for high-rate indoor VLC systems. Multiple data streams can be transmitted simultaneously in the proposed scheme. Based on the transmit strategy, we develop a low-complexity receiver algorithm that achieves better bit-error rate performance than reference schemes, and the proposed OSM scheme has a much lower PAPR than OFDM based OSM schemes. When the spatial subchannels are highly correlated, a spatial area division strategy is applied, and the receiver algorithm is investigated. The symbol-error rate expression of the proposed OSM scheme is derived, and the computational complexity is analyzed.

Effective Receiver Design for MIMO Visible Light Communication with Quadrichromatic LEDs

In this paper, we propose a receiver designing principle for the multiple-input multiple-output (MIMO) visible light communication (VLC) systems with quadrichromatic light emitting diodes (QLEDs). To simultaneously transmit multiple data streams, the system consists of multiple QLEDs and multiple receivers; each includes four photodiodes (PDs). We optimize the Euclidean distance of the received signal over some constraints by considering the normal vectors of the receivers. The numerical results show that our proposed receiver design has a better bit error rate (BER) performance in comparison with the conventional receiver.

Precision Medicine in Pancreatic Disease-Knowledge Gaps and Research Opportunities: Summary of a National Institute of Diabetes and Digestive and Kidney Diseases Workshop.

A workshop on research gaps and opportunities for Precision Medicine in Pancreatic Disease was sponsored by the National Institute of Diabetes and Digestive Kidney Diseases on July 24, 2019, in Pittsburgh. The workshop included an overview lecture on precision medicine in cancer and 4 sessions: (1) general considerations for the application of bioinformatics and artificial intelligence; (2) omics, the combination of risk factors and biomarkers; (3) precision imaging; and (4) gaps, barriers, and needs to move from precision to personalized medicine for pancreatic disease. Current precision medicine approaches and tools were reviewed, and participants identified knowledge gaps and research needs that hinder bringing precision medicine to pancreatic diseases. Most critical were (a) multicenter efforts to collect large-scale patient data sets from multiple data streams in the context of environmental and social factors; (b) new information systems that can collect, annotate, and quantify data to inform disease mechanisms; (c) novel prospective clinical trial designs to test and improve therapies; and (d) a framework for measuring and assessing the value of proposed approaches to the health care system. With these advances, precision medicine can identify patients early in the course of their pancreatic disease and prevent progression to chronic or fatal illness.

Optimization of MIMO Device-to-Device Networks via Matrix Fractional Programming: A Minorization–Maximization Approach

Interference management is a fundamental issue in device-to-device (D2D) communications whenever the transmitter-and-receiver pairs are located in close proximity and frequencies are fully reused, so active links may severely interfere with each other. This paper devises an optimization strategy named FPLinQ to coordinate the link scheduling decisions among the interfering links, along with power control and beamforming. The key enabler is a novel optimization method called matrix fractional programming (FP) that generalizes previous scalar and vector forms of FP in allowing multiple data streams per link. From a theoretical perspective, this paper provides a deeper understanding of FP by showing a connection to the minorization-maximization (MM) algorithm. From an application perspective, this paper shows that as compared to the existing methods for coordinating scheduling in the D2D network, such as FlashLinQ, ITLinQ, and ITLinQ+, the proposed FPLinQ approach is more general in allowing multiple antennas at both the transmitters and the receivers, and further in allowing arbitrary and multiple possible associations between the devices via matching. Numerical results show that FPLinQ significantly outperforms the previous state-of-the-art in a typical D2D communication environment.

Three decades of simulated global terrestrial carbon fluxes from a data assimilation system confronted with different periods of observations

Abstract. During the last decade, carbon cycle data assimilation systems (CCDAS) have focused on improving the simulation of seasonal and mean global carbon fluxes over a few years by simultaneous assimilation of multiple data streams. However, the ability of a CCDAS to predict longer-term trends and variability of the global carbon cycle and the constraint provided by the observations have not yet been assessed. Here, we evaluate two near-decade-long assimilation experiments of the Max Planck Institute – Carbon Cycle Data Assimilation System (MPI-CCDAS v1) using spaceborne estimates of the fraction of absorbed photosynthetic active radiation (FAPAR) and atmospheric CO2 concentrations from the global network of flask measurement sites from either 1982 to 1990 or 1990 to 2000. We contrast these simulations with independent observations from the period 1982–2010, as well as a third MPI-CCDAS assimilation run using data from the full 1982–2010 period, and an atmospheric inversion covering the same data and time. With 30 years of data, MPI-CCDAS is capable of representing land uptake to a sufficient degree to make it compatible with the atmospheric CO2 record. The long-term trend and seasonal amplitude of atmospheric CO2 concentrations at station level over the period 1982 to 2010 is considerably improved after assimilating only the first decade (1982–1990) of observations. After 15–19 years of prognostic simulation, the simulated CO2 mixing ratio in 2007–2010 diverges by only 2±1.3 ppm from the observations, the atmospheric inversion, and the MPI-CCDAS assimilation run using observations from the full period. The long-term trend, phenological seasonality, and interannual variability (IAV) of FAPAR in the Northern Hemisphere over the last 1 to 2 decades after the assimilation were also improved. Despite imperfections in the representation of the IAV in atmospheric CO2, model–data fusion for a decade of data can already contribute to the prognostic capacity of land carbon cycle models at relevant timescales.

Performance evaluation of image transmission using diversity selection combining technique

This paper discussed a simulation of SPIHT image compression transmission on wireless channels by using RS code and diversity selection combining technique in wavelet domain to get better reconstruction image by minimizing errors that caused by fading and noise. RS Code and Diversity Selection Combining Method were used to combat errors during image transmission on wireless channels. When the bit stream data arrived at the receiver, the coding was used to recover the errors in the bitstream. Diversity was used to obtain multiple data streams corresponding to the transmitted image at the receiver. These individual image data streams were combined to form a composite image with the better quality. The results show both methods demonstrated quality improvement of the received image.

USE: An integrative suite for temporally-precise psychophysical experiments in virtual environments for human, nonhuman, and artificially intelligent agents

BackgroundThere is a growing interest in complex, active, and immersive behavioral neuroscience tasks. However, the development and control of such tasks present unique challenges. New MethodThe Unified Suite for Experiments (USE) is an integrated set of hardware and software tools for the design and control of behavioral neuroscience experiments. The software, developed using the Unity video game engine, supports both active tasks in immersive 3D environments and static 2D tasks used in more traditional visual experiments. The custom USE SyncBox hardware, based around an Arduino Mega2560 board, integrates and synchronizes multiple data streams from different pieces of experimental hardware. The suite addresses three key issues with developing cognitive neuroscience experiments in Unity: tight experimental control, accurate sub-ms timing, and accurate gaze target identification. ResultsUSE is a flexible framework to realize experiments, enabling (i) nested control over complex tasks, (ii) flexible use of 3D or 2D scenes and objects, (iii) touchscreen-, button-, joystick- and gaze-based interaction, and (v) complete offline reconstruction of experiments for post-processing and temporal alignment of data streams. Comparison with Existing MethodsMost existing experiment-creation tools are not designed to support the development of video-game-like tasks. Those that do use older or less popular video game engines as their base, and are not as feature-rich or enable as precise control over timing as USE. ConclusionsUSE provides an integrated, open source framework for a wide variety of active behavioral neuroscience experiments using human and nonhuman participants, and artificially-intelligent agents.

Artificial intelligence in the interpretation of breast cancer on MRI

Advances in both imaging and computers have led to the rise in the potential use of artificial intelligence (AI) in various tasks in breast imaging, going beyond the current use in computer-aided detection to include diagnosis, prognosis, response to therapy, and risk assessment. The automated capabilities of AI offer the potential to enhance the diagnostic expertise of clinicians, including accurate demarcation of tumor volume, extraction of characteristic cancer phenotypes, translation of tumoral phenotype features to clinical genotype implications, and risk prediction. The combination of image-specific findings with the underlying genomic, pathologic, and clinical features is becoming of increasing value in breast cancer. The concurrent emergence of newer imaging techniques has provided radiologists with greater diagnostic tools and image datasets to analyze and interpret. Integrating an AI-based workflow within breast imaging enables the integration of multiple data streams into powerful multidisciplinary applications that may lead the path to personalized patient-specific medicine. In this article we describe the goals of AI in breast cancer imaging, in particular MRI, and review the literature as it relates to the current application, potential, and limitations in breast cancer. Level of Evidence: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:1310-1324.

Generating intra-year metrics of wildfire progression using multiple open-access satellite data streams

The 2017 fire season was one of the largest on record for British Columbia (BC), Canada, in terms of total area burned (estimated 1.2 million ha), affecting the safety and air quality of numerous communities. Moreover, fires of this number and extent alter the wood supply for harvesting, the nature of habitat for wildlife, and can affect regional and national carbon budgets. As a result, it is important to map these fires accurately and to monitor within-year fire progression in order to quantify the resulting forest-disturbance impacts fully. The Bayesian Updating of Land Cover (BULC) algorithm was used to merge burned-area classifications of individual fires from a range of remote sensing sources such as Landsat-7, Landsat-8, Sentinel-2, and MODIS (MCD64A1) burned-area dataset. Together, these provisional classifications imaged each pixel within a known fire perimeter an average of 33.8 times between April 1 and December 1. The resulting 35-week time-series stack had updated weekly burned areas for each of the 89 fires in BC in 2017. Province-wide fire progression was variable throughout the period analyzed, characterized by a steady burn phase of 41,437 ha (5% of total area burned) over a two-week period in early July, an accelerated burn phase of 149,422 ha (17%) from mid-July to early August, another steady burn phase of 218,079 ha (24%) for one month until early September, and a second accelerated burn phase of 301,931 ha (34%) over two weeks in late September with subsequent steady growth of 180,119 ha (20%) over 1.5 months until containment in late October. Herein, we demonstrate how such temporally dense fire classification stacks can be used to analyze fire progression over the course of a fire season (both retrospectively and in near-real time) providing useful metrics to characterize and compare fire events. End-of-season burned-area estimates correspond with estimates derived from the National Burned Area Composite (NBAC) product that is generated retrospectively from fire best-available mapping approaches. This rapid interpretation of information enables the analysis of suppression success and potential drivers of fires spread while facilitating analyses of carbon budget consequences as well as impacts to communities and timber supply.

Joint modelling of multi‐scale animal movement data using hierarchical hidden Markov models

Abstract Hidden Markov models are prevalent in animal movement modelling, where they are widely used to infer behavioural modes and their drivers from various types of telemetry data. To allow for meaningful inference, observations need to be equally spaced in time, or otherwise regularly sampled, where the corresponding temporal resolution strongly affects what kind of behaviours can be inferred from the data. Recent advances in biologging technology have led to a variety of novel telemetry sensors which often collect data from the same individual simultaneously at different time‐scales, for example step lengths obtained from GPS tags every hour, dive depths obtained from time‐depth recorders once per dive, or accelerations obtained from accelerometers several times per second. However, to date, statistical machinery to address the corresponding complex multi‐stream and multi‐scale data is lacking. We propose hierarchical hidden Markov models as a versatile statistical framework that naturally accounts for differing temporal resolutions across multiple variables. In these models, the observations are regarded as stemming from multiple connected behavioural processes, each of which operates at the time‐scale at which the corresponding variables were observed. By jointly modelling multiple data streams, collected at different temporal resolutions, corresponding models can be used to infer behavioural modes at multiple time‐scales and in particular, help to draw a much more comprehensive picture of an animal's movement patterns, for example with regard to long‐term versus short‐term movement strategies. The suggested approach is illustrated in two real‐data applications, where we jointly model (a) coarse‐scale horizontal and fine‐scale vertical Atlantic cod Gadus morhua movements throughout the English Channel, and (b) coarse‐scale horizontal movements and corresponding fine‐scale accelerations of a horn shark Heterodontus francisci tagged off the Californian coast.

Singular value decomposition assisted multicarrier continuous-variable quantum key distribution

We define the singular value decomposition (SVD) assisted multicarrier continuous-variable quantum key distribution (CVQKD) protocol. The proposed protocol uses the singular value decomposition of the Gaussian quantum channel, which yields an additional degree of freedom for the phase space transmission. This additional degree of freedom can further be exploited in a multiple-access scenario. The SVD-assistance defines the eigenchannels of the Gaussian physical link, which can be used for the simultaneous reliable transmission of multiple user data streams. Our transmission model also includes the singular interference avoider (SIA) precoding scheme. The proposed SIA precoding scheme prevents the eigenchannel interference to reach an optimal transmission over a Gaussian link. We demonstrate the results through the adaptive multicarrier quadrature division–multiuser quadrature allocation (AMQD-MQA) CVQKD multiple-access scheme. The singular value assisted transmission provides improved simultaneous transmission rates for the users, particularly in crucial low signal-to-noise ratio regimes.

Asymptotically Optimal Quickest Change Detection in Multistream Data—Part 1: General Stochastic Models

Assume that there are multiple data streams (channels, sensors) and in each stream the process of interest produces generally dependent and non-identically distributed observations. When the process is in a normal mode (in-control), the (pre-change) distribution is known, but when the process becomes abnormal there is a parametric uncertainty, i.e., the post-change (out-of-control) distribution is known only partially up to a parameter. Both the change point and the post-change parameter are unknown. Moreover, the change affects an unknown subset of streams, so that the number of affected streams and their location are unknown in advance. A good changepoint detection procedure should detect the change as soon as possible after its occurrence while controlling for a risk of false alarms. We consider a Bayesian setup with a given prior distribution of the change point and propose two sequential mixture-based change detection rules, one mixes a Shiryaev-type statistic over both the unknown subset of affected streams and the unknown post-change parameter and another mixes a Shiryaev–Roberts-type statistic. These rules generalize the mixture detection procedures studied by Tartakovsky (IEEE Trans Inf Theory 65(3):1413–1429, 2019) in a single-stream case. We provide sufficient conditions under which the proposed multistream change detection procedures are first-order asymptotically optimal with respect to moments of the delay to detection as the probability of false alarm approaches zero.

Reconfigurable Modular Computing System

Purpose of research. A reconfigurable computer system consists of a computing system and special-purpose computers that are used to solve the tasks of vector and matrix algebra, pattern recognition. There are distinctions between matrix and associative systems, neural networks. Matrix computing systems comprise a set of processor units connected through a switching device with multi-module memory. They are designed to solve vector, matrix and data array problems. Associative systems contain a large number of operating devices that can simultaneously process multiple data streams. Neural networks and neurocomputers have high performance when solving problems of expert systems, pattern recognition due to parallel processing of a neural network.Methods. An information graph of the computational process of a reconfigurable modular system was plotted. Structural and functional schemes, algorithms that implement the construction of specialized modules for performing arithmetic and logical operations, search operations and functions for replacing occurrences in processed words were developed. Software for modelling the operation of the arithmetic-symbol processor, specialized computing modules, and switching systems was developed.Results. A block diagram of a reconfigurable computing modular system was developed. The system consists of compatible functional modules and is capable of static and dynamic reconfiguration, has a parallel connection structure of the processor and computing modules through the use of interface channels. It consists of an arithmeticsymbol processor, specialized computing modules and switching systems; it performs specific tasks of symbolic information processing, arithmetic and logical operations.Conclusion. Systems with a reconfigurable structure are high-performance and highly reliable computing systems that consist of integrated processors in multi-machine and multiprocessor systems. Reconfigurability of the structure provides high system performance due to its adaptation to computational processes and the composition of the processed tasks.

Abstract 4188: Registering cancer recurrence in a population-based registry: The value of pathology data

Abstract INTRODUCTION No population-wide cancer registry systematically records recurrence. It is often requested of U.S. registries, but cannot be provided. To fill this gap, the Georgia Cancer Registry (GCR) has embarked on a five-year project to add recurrence data to the registry for early-stage breast, prostate, colorectal cancer and lymphoma patients. Multiple data streams will be used to provide signals of recurrence, which will then be validated by a Certified Tumor Registrar from the registry staff. Electronic pathology data are submitted in real-time to the GCR and could be a key data stream to signal recurrences. METHODS We created cohorts of early-stage breast and colorectal cancer patients treated at Commission on Cancer (CoC) facilities in Georgia and for whom the CoC facility both reported their pathology data to the GCR electronically and had documented a recurrence in the patient’s record. A randomly selected sample (n = 60) from each cohort was linked to electronic pathology (E-Path) reports in the registry to evaluate a) the proportion of patients with a report available in the GCR within 10 days of the documented recurrence and b) the proportion of patients whose E-Path report included the specific term(s) “recurrent”, or “recurrence(s)”. All pathology reports were manually reviewed to make the above determinations. RESULTS All 60 cases from each cancer site had E-Path reports available for the incident cancer that was diagnosed. Upon manual review, 67% of breast cases and 63% of colorectal cases had reports in the registry within 10 days of the recurrence date reported by the CoC facility. When examining all reports for a given patient, regardless of date, for use of the specific term(s) “recurrent”, or “recurrence(s)”, only 18% of breast cases and 9% of colorectal cases contained these exact terms. CoC facilities also attempt to document the location of the recurrence in addition to the date. For cases with reports available in the registry within 10 days of the recurrence reported by the CoC facility, confirmation of the recurrence location was documented in the pathology report for the majority of patients (78% for breast and 89% for colon). DISCUSSION E-Path reports will be an important source of information to signal recurrences, but the specific use of recurrence terminology was limited in the sample of reports selected for this study. Use of the terms “metastatic” or documentation of other organs as “positive for malignancy” was much more common. Not all recurrences will be signaled by E-Path reports, so the use of additional data streams as planned by this project will be critical and validation of these signals by trained staff will establish the gold standard of true recurrences. This ambitious project is just launching, with the aim of providing the first-ever descriptive data about recurrence rates for four common cancers over up to ten years of follow-up. Citation Format: Leah Moubadder, Audrey Chang, Kevin C. Ward, Timothy L. Lash. Registering cancer recurrence in a population-based registry: The value of pathology data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4188.