Using Machine Learning to Streamline Study Initiation and Setup

Clinical trials are critical for developing new medical treatments, but the initiation and setup phases are often plagued by inefficiencies and delays. Traditional methods rely heavily on manual processes, which can be slow and error-prone. Machine Learning offers promising solutions to these challenges by automating and optimizing various aspects of study initiation, from protocol design to site selection and participant recruitment. This review highlights the key areas where ML can streamline study initiation and setup, providing a comprehensive overview of its impact on clinical research.

1. Welcome
USING MACHINE LEARNING TO STREAMLINE STUDY
INITIATION & SETUP
NakkaTeja
B.Pharmacy
Student id: 049/052024
10/18/2022
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2. Index
•What is CR?
•Introduction
•Process
•Demonstration Topics
•Conclusion
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3. What is CR?
•A Clinical Study Report (CSR) is a document that describes the methods and
results of a clinical study or trial, along with a short discussion of key findings
related to the study.
•Comprehensive report of an individual study conducted in patients of any
therapeutic, prophylactic, or diagnostic agent.
•A CSR must include an explanation of critical design features of the study,
methods and how the study was carried out, individual patient data, and details of
analytical methods. They are submitted to regulatory authorities and ethics
committees and are a key element of submissions for regulatory approval.
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4. Introduction
•Machine learning in clinical research represents a significant advancement in life
sciences industry. By leveraging ML algorithms, researchers can streamline patient
recruitment, optimize trial design, uncover valuable insights through data analysis,
and implement adaptive trial strategies.
•What are the benefits of machine learning in clinical trials?
•The result is more efficient and informative trials, reducing costs, expediting
timelines, and ultimately increasing the likelihood of successful outcomes.
Additionally, the ability to analyze complex data enables researchers to design trials
that minimize patient burden and enhance participant retention.
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5. PROCESS
Using Machine learning to stream line study Initiation and set up
• Machine learning is an application of
Artificial intelligence that involves
algorithms and data that automatically
analyses and make decisions by itself
without human intervention
• Therefore we can say in machine
language Artificial intelligence is
generated on the basis of experience
• It describes how computer perform tasks
on their own by previous experience
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6. Normal Computer vs ML
The difference between normal computer
software and machine learning is that a
human developer hasn’t given code that
instructs the system how to react to the
situation ,instead it is being trained by a large
number of data
Difference between ML & AI
AI is a concept of creating intelligence
machinesthat stimulates human behaviour
Whereas Machine learning is a subset of AI
that allows machine to learn from data
without being programmed
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7. •Why data science and the use of machine learning is
essential to pharmaceutical, biotech, and medical
device organizations
•Practical approaches for interference or predication
through machine learning
•Metrics to evaluate to performance to prediction
made possibilities for future of machine learning in
clinical research
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8. Demonstration Topics
•RstudioEnvironment
•Demonstration Use Case
-Simulated Mini-Mental State
Examination data
-Time to first dermatological event
•Data Preparation
-ETL Load datasets/
manipulate datasets
•Inference
•Prediction
•Conclusion
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9. Streamlining study startup
•Study Start-up is a priority Focus
•83% of clinical taking action to streamline study start-up
•This is particularly important amid the rapid globalization
of drug development, where access to the right
intelligence during study start-up is critical.
•Intelligence has evolved to go beyond the typical site-level
information. It is now captured at the country level, which
allows for better feasibility outcomes. Intelligence also
now incorporates the regulatory environment risks; the
timelines for site contracting; and budget negotiation, as
well as submissions, and even time to schedule critical
path visits, such as pre-study and site initiation visits. In
addition, site intelligence consists of enrollment
capabilities and the site's ability to meet enrollment goals.
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10. Strategies evolve
•Amid the growing challenges in study start-up,
strategies in this area are increasingly emphasizing
workflow management as opposed to simply the
tracking of data
•During the clinical trial, measuring against
performance is key to ensuring baseline timelines and
plans are managed appropriately and variability is
understood
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11. Technology push
•Global reporting systems and technologies in clinical
development have advanced considerably over the past
decade, largely shifting from the tedious exercise of
compiling Excel spreadsheets to technologies that allow
for more real-time data and reporting
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12. Organizations Aim to speed study start -Up
•Top drivers to improve study start-up
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13. Conclusion
•Much more is possible
•With machine learning algorithms applied to end goals of inference or
prediction, knowledge can increase of subgroups of interest and on outcomes
before they occur
•Combining real-world data with clinical data can grant endless possibilities
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14. Thank You!
www.clinosol.com
(India | Canada)
9121151622/623/624
[email protected]
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