Online Analysis of High-Volume Data Streams in Astroparticle Physics

2015 ◽  
pp. 100-115 ◽  
Author(s):  
Christian Bockermann ◽  
Kai Brügge ◽  
Jens Buss ◽  
Alexey Egorov ◽  
Katharina Morik ◽  
...  
Keyword(s):  
Data Streams ◽  
High Volume ◽  
Volume Data ◽  
Astroparticle Physics ◽  
Online Analysis

Scalable distance-based outlier detection over high-volume data streams

2014 ◽  
Author(s):  
Lei Cao ◽  
Di Yang ◽  
Qingyang Wang ◽  
Yanwei Yu ◽  
Jiayuan Wang ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Data Streams ◽  
High Volume ◽  
Volume Data

scholarly journals A Parameter Space Framework for Online Outlier Detection Over High-Volume Data Streams

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 38124-38136 ◽  
Author(s):  
Guanzhe Zhao ◽  
Yanwei Yu ◽  
Peng Song ◽  
Geng Zhao ◽  
Zhe Ji
Keyword(s):  
Outlier Detection ◽  
Parameter Space ◽  
Data Streams ◽  
High Volume ◽  
Volume Data

scholarly journals Monoclonal Antibody-Based Immunotherapy and Its Role in the Development of Cardiac Toxicity

Cancers ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 86
Author(s):  
Mohit Kumar ◽  
Chellappagounder Thangavel ◽  
Richard C. Becker ◽  
Sakthivel Sadayappan
Keyword(s):  
Cancer Patients ◽  
Immune Modulation ◽  
Checkpoint Inhibitors ◽  
Survival Rates ◽  
Induced Pluripotent Stem Cell ◽  
High Volume ◽  
Oncolytic Viruses ◽  
Model Systems ◽  
Volume Data ◽  
Late Cardiotoxicity

Immunotherapy is one of the most effective therapeutic options for cancer patients. Five specific classes of immunotherapies, which includes cell-based chimeric antigenic receptor T-cells, checkpoint inhibitors, cancer vaccines, antibody-based targeted therapies, and oncolytic viruses. Immunotherapies can improve survival rates among cancer patients. At the same time, however, they can cause inflammation and promote adverse cardiac immune modulation and cardiac failure among some cancer patients as late as five to ten years following immunotherapy. In this review, we discuss cardiotoxicity associated with immunotherapy. We also propose using human-induced pluripotent stem cell-derived cardiomyocytes/ cardiac-stromal progenitor cells and cardiac organoid cultures as innovative experimental model systems to (1) mimic clinical treatment, resulting in reproducible data, and (2) promote the identification of immunotherapy-induced biomarkers of both early and late cardiotoxicity. Finally, we introduce the integration of omics-derived high-volume data and cardiac biology as a pathway toward the discovery of new and efficient non-toxic immunotherapy.

Transforming data into information

2003 ◽  
Vol 47 (2) ◽  
pp. 43-51 ◽  
Author(s):  
M.B. Beck ◽  
Z. Lin
Keyword(s):  
High Volume ◽  
Recursive Estimation ◽  
Data Sets ◽  
Monitoring Networks ◽  
Volume Data ◽  
Demand Pull ◽  
Variable Character ◽  
History Of ◽  
On Line ◽  
Automated Instruments

In spite of a long history of automated instruments being deployed in the water industry, only recently has the difficulty of extracting timely insights from high-grade, high-volume data sets become an important problem. Put simply, it is now relatively easy to be “data-rich”, much less easy to become “information-rich". Whether the availability of so many data arises from “technological push” or the “demand pull” of practical problem solving is not the subject of discussion. The paper focuses instead on two issues: first, an outline of a methodological framework, based largely on the algorithms of (on-line) recursive estimation and involving a sequence of transformations to which the data can be subjected; and second, presentation and discussion of the results of applying these transformations in a case study of a biological system of wastewater treatment. The principal conclusion is that the difficulty of transforming data into information may lie not so much in coping with the high sampling intensity enabled by automated monitoring networks, but in coming to terms with the complexity of the higher-order, multi-variable character of the data sets, i.e., in interpreting the interactions among many contemporaneously measured quantities.

scholarly journals Defeating information overload in health surveillance using a metacognitive aid innovation from military combat systems

2016 ◽  
Vol 14 (4) ◽  
pp. 371-388 ◽  
Author(s):  
Daniel C McFarlane ◽  
Alexa K Doig ◽  
James A Agutter ◽  
Jonathan L Mercurio ◽  
Ranjeev Mittu ◽  
...  
Keyword(s):  
Decision Making ◽  
Information Overload ◽  
High Volume ◽  
Health Surveillance ◽  
Simulated Patients ◽  
Naval Research ◽  
Hospital Nurses ◽  
Volume Data ◽  
Front Line ◽  
Training Requirement

Modern sensors for health surveillance generate high volumes and rates of data that currently overwhelm operational decision-makers. These data are collected with the intention of enabling front-line clinicians to make effective clinical judgments. Ironically, prior human–systems integration (HSI) studies show that the flood of data degrades rather than aids decision-making performance. Health surveillance operations can focus on aggregate changes to population health or on the status of individual people. In the case of clinical monitoring, medical device alarms currently create an information overload situation for front-line clinical workers, such as hospital nurses. Consequently, alarms are often missed or ignored, and an impending patient adverse event may not be recognized in time to prevent crisis. One innovation used to improve decision making in areas of data-rich environments is the Human Alerting and Interruption Logistics (HAIL) technology, which was originally sponsored by the US Office of Naval Research. HAIL delivers metacognitive HSI services that empower end-users to quickly triage interruptions and dynamically manage their multitasking. HAIL informed our development of an experimental prototype that provides a set of context-enabled alarm notification services (without automated alarm filtering) to support users’ metacognition for information triage. This application is called HAIL Clinical Alarm Triage (HAIL-CAT) and was designed and implemented on a smartwatch to support the mobile multitasking of hospital nurses. An empirical study was conducted in a 20-bed virtual hospital with high-fidelity patient simulators. Four teams of four registered nurses (16 in total) participated in a 180-minute simulated patient care scenario. Each nurse was assigned responsibility to care for five simulated patients and high rates of simulated health surveillance data were available from patient monitors, infusion pumps, and a call light system. Thirty alarms per nurse were generated in each 90-minute segment of the data collection sessions, only three of which were clinically important alarms. The within-subjects experimental design included a treatment condition where the nurses used HAIL-CAT on a smartwatch to triage and manage alarms and a control condition without the smartwatch. The results show that, when using the smartwatch, nurses responded three times faster to clinically important and actionable alarms. An analysis of nurse performance also shows no negative effects on their other duties. Subjective results show favorable opinions about utility, usability, training requirement, and adoptability. These positive findings suggest the potential for the HAIL HSI system to be transferrable to the domain of health surveillance to achieve the currently unrealized potential utility of high-volume data.

REAL-TIME HIGH-VOLUME DATA TRANSFER AND PROCESSING FOR e-VLBI

2010 ◽  
pp. 81-90
Author(s):  
YASUHIRO KOYAMA ◽  
TETSURO KONDO ◽  
MORITAKA KIMURA ◽  
MASAKI HIRABARU ◽  
HIROSHI TAKEUCHI
Keyword(s):  
Real Time ◽  
Data Transfer ◽  
High Volume ◽  
Volume Data

Creating an online resource providing hospital cancer surgery volumes in California.

2016 ◽  
Vol 34 (7_suppl) ◽  
pp. 172-172
Author(s):  
Christina A Clarke ◽  
Laurence C Baker ◽  
Jennifer Malin ◽  
Joseph Parker ◽  
Merry Holliday-Hanson ◽  
...  
Keyword(s):  
Health Planning ◽  
Public Information ◽  
High Volume ◽  
Median Number ◽  
Cancer Surgery ◽  
Sufficient Evidence ◽  
Cancer Site ◽  
Volume Data ◽  
Volume Calculation ◽  
Low Volume

172 Background: Little evidence is available to help patients and providers, payers and policymakers find the highest-quality hospitals for cancer surgery. We initiated a groundbreaking effort in California ( www.calqualitycare.org ) to publicly report hospital cancer surgery volume data online. Methods: With financial support from the nonprofit California HealthCare Foundation, we assembled a multidisciplinary team to oversee the project and ensure sound methodology. We obtained existing hospital discharge summary data from the California Office of Statewide Health Planning and Development (OSHPD). We selected cancer surgeries eligible for display through comprehensive review of the literature addressing the association of hospital volume and mortality. We found eleven cancer sites with sufficient evidence of association including bladder, brain, breast, colon, esophagus, liver, lung, pancreas, prostate, rectum, and stomach. Experts advised volume calculation and display of results. Leaders of low volume hospitals were interviewed to understand the reasons for low volume. Results: In 2014, about 60% of cancer surgeries in California were performed at hospitals in the top 20% of volume, but many hospitals performed low numbers of complex procedures, with the per hospital median number of surgeries for esophageal, pancreatic, stomach, liver, or bladder cancer surgeries at 4 or less. Low-volume hospitals included rural and urban hospitals, with small and large bed sizes, and teaching and non-teaching status. At least 670 Californians received cancer surgery at hospitals that performed only one or two surgeries for a particular cancer site; 72% of those patients lived within 50 miles of a top-20% volume hospital. Conclusions: This project demonstrates the potential for public information about hospital volumes to point patients towards high-volume and away from low-volume hospitals. Data regarding 2014 volumes are now available online.

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