How to Transform Clinical Trial Management with Advanced Data Analytics

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In the fast-evolving landscape of clinical trials, CROs stand at the forefront of innovation and operational excellence. However, navigating the complexities of trial management, from patient recruitment to regulatory compliance, presents a unique set of challenges.

Data & Analytics

How to Transform
Clinical Trial
Management
A Modern Data Approach

Housekeeping
• 30-minute presentation with Q&A
• Type questions into the "question box" and submit
throughout the presentation
• We'll send a copy of the deck and recording of the
webinar in follow-up emails after the event
Some key items before we get started

Meet Today’s
Presenters
Sr. Data Architect
Ralph Jaquez
Lead BI Analyst
Meaghan Frost

About
BrainSell
The Growth Enablement Company
Blueprint Digital Transformation
• Digital Strategy
• Process Mapping
• Change Management
Leverage Data as an Asset
• Modern Data Platform
• Unified Reporting
• Executive Dashboards
Modernize Technology
• Enterprise-level Architecture
• Automation
• Artificial Intelligence (AI)
We help companies grow and scale.

Agend
a 01 The Data Challenge for CROs
02 A Modern Approach
03 Data Spotlight
04 Q&A

Let’s Take a
Poll
1. Super spreadsheets that are hard to maintain
2. Reports with numbers that don’t match
3. Stale dashboards or reports
4. Collecting data from multiple systems
5. Taking more time than I’d like to collect and present
the data
What symptoms related to data are you experiencing?

Data Challenges for
Clinical Research
Organizations

• The volume and complexity of data has exploded
• Data collected in clinical trials contributes to the body of evidence used
to support new medical interventions
• Data helps to maintain compliance with regulatory requirements
• Data is used to track quality assurance and monitor integrity and
validity throughout the clinical trial
Data: The Foundation of Clinical Trials and
Research

X Reliance on spreadsheets
X Multiple data sources create complexity
X Data quality and accuracy
X Resource constraints for building reports/dashboards
X Long lead times to compile and deliver reports
X Duplicated effort producing similar reports
Common Challenges

 Centralized source of truth
 Multiple data sources integrated and organized
 Timely delivery of data and reports
 Identify data quality issues faster
 Improve data accuracy
 Identify areas for process improvement
The Outcome

Email growth@brainsell.com to schedule your assessment
Data Readiness
Assessment
Assess your organization's current state
Understand where you are in your data driven
journey
Receive tactical guidance to help accelerate your
data modernization solutions
Live demo of example solutions

Audience Q&A
Have questions? We have answers.

THANK YOU
FOR JOINING
US!
(866) 356-2654 growth@brainsell.com www.brainsell.com

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Malana- Gimlet Market Analysis (Portfolio 2)

TravisMalana

Machine learning and optimization techniques for electrical drives.pptx

balafet

一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单

ewymefz

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一比一原版(CBU毕业证)卡普顿大学毕业证成绩单

nscud

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Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...

Subhajit Sahu

Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.

一比一原版(BU毕业证)波士顿大学毕业证成绩单

ewymefz

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一比一原版(CBU毕业证)卡普顿大学毕业证如何办理

ahzuo

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Opendatabay - Open Data Marketplace.pptx

Opendatabay

Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets. First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience. From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets. Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.

Adjusting primitives for graph : SHORT REPORT / NOTES

Subhajit Sahu

Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is Multiply with different modes (map) 1. Performance of sequential execution based vs OpenMP based vector multiply. 2. Comparing various launch configs for CUDA based vector multiply. Sum with different storage types (reduce) 1. Performance of vector element sum using float vs bfloat16 as the storage type. Sum with different modes (reduce) 1. Performance of sequential execution based vs OpenMP based vector element sum. 2. Performance of memcpy vs in-place based CUDA based vector element sum. 3. Comparing various launch configs for CUDA based vector element sum (memcpy). 4. Comparing various launch configs for CUDA based vector element sum (in-place). Sum with in-place strategies of CUDA mode (reduce) 1. Comparing various launch configs for CUDA based vector element sum (in-place).

Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...

pchutichetpong

M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years. Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success. MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies. According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”

Ch03-Managing the Object-Oriented Information Systems Project a.pdf

haila53

一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单

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Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...

Subhajit Sahu

Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.

Best best suvichar in gujarati english meaning of this sentence as Silk road ...

AbhimanyuSinha9

SOCRadar Germany 2024 Threat Landscape Report

SOCRadar

As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape. In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity. 🔑 Key findings include: 🔍 Increased frequency and complexity of cyber threats. 🔍 Escalation of state-sponsored and criminally motivated cyber operations. 🔍 Active dark web exchanges of malicious tools and tactics. Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities. This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.

一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单

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一比一原版(Deakin毕业证书)迪肯大学毕业证如何办理

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How to Transform Clinical Trial Management with Advanced Data Analytics

1. How to Transform Clinical Trial Management A Modern Data Approach

2. Housekeeping • 30-minute presentation with Q&A • Type questions into the "question box" and submit throughout the presentation • We'll send a copy of the deck and recording of the webinar in follow-up emails after the event Some key items before we get started

3. Meet Today’s Presenters Sr. Data Architect Ralph Jaquez Lead BI Analyst Meaghan Frost

4. About BrainSell The Growth Enablement Company Blueprint Digital Transformation • Digital Strategy • Process Mapping • Change Management Leverage Data as an Asset • Modern Data Platform • Unified Reporting • Executive Dashboards Modernize Technology • Enterprise-level Architecture • Automation • Artificial Intelligence (AI) We help companies grow and scale.

5. Agend a 01 The Data Challenge for CROs 02 A Modern Approach 03 Data Spotlight 04 Q&A

6. Let’s Take a Poll 1. Super spreadsheets that are hard to maintain 2. Reports with numbers that don’t match 3. Stale dashboards or reports 4. Collecting data from multiple systems 5. Taking more time than I’d like to collect and present the data What symptoms related to data are you experiencing?

7. Data Challenges for Clinical Research Organizations

8. • The volume and complexity of data has exploded • Data collected in clinical trials contributes to the body of evidence used to support new medical interventions • Data helps to maintain compliance with regulatory requirements • Data is used to track quality assurance and monitor integrity and validity throughout the clinical trial Data: The Foundation of Clinical Trials and Research

9. X Reliance on spreadsheets X Multiple data sources create complexity X Data quality and accuracy X Resource constraints for building reports/dashboards X Long lead times to compile and deliver reports X Duplicated effort producing similar reports Common Challenges

10. Traditional Integration Methods

11. A Modern Approach

12. The Solution

13.  Centralized source of truth  Multiple data sources integrated and organized  Timely delivery of data and reports  Identify data quality issues faster  Improve data accuracy  Identify areas for process improvement The Outcome

14. Data Spotlight

15.

16. Email growth@brainsell.com to schedule your assessment Data Readiness Assessment Assess your organization's current state Understand where you are in your data driven journey Receive tactical guidance to help accelerate your data modernization solutions Live demo of example solutions

17. Audience Q&A Have questions? We have answers.

18. THANK YOU FOR JOINING US! (866) 356-2654 growth@brainsell.com www.brainsell.com

Editor's Notes

Ralph
Ralph
Meaghan
Sarah to facilitate questions to Ralph and Meaghan

How to Transform Clinical Trial Management with Advanced Data Analytics

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