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Artificial Intelligence to Solve Clinical's "Big Data" Challenges:
Practical Today, Predicting Tomorrow
Panel:
Catherine Celingant, Pfizer
Jason Raines, Apellis
Raj Indupuri, eClinical Solutions
Chair & Moderator:
Sam Anwar, eClinical Solutions
Our Panelists
Catherine Celingant Raj IndupuriJason Raines Sam Anwar
Sr Director, Data
Monitoring &
Management
Pfizer
VP, Data & Digital
Apellis
CEO
eClinical Solutions
Chief Technology
Officer
eClinical Solutions
Agenda
• Introduction
• Application of AI across industries
• Polling questions
• Use cases
• Panel Discussion
• Q&A
Introduction
Innovation Innovation
Disruption Disruption
VHS
Media
Streaming
AI Today: A Collection of Tech
"A Framework for Applying AI in the Enterprise" (G00336031) Bern Elliot and Whit Andrews
AI is ________.
What business problem
are you trying to solve?
RPA NLP
ML
Vision Logic
Robots and
Sensors
Robots and
Sensors
Machine
Learning,
Deep
Learning,
Neural
Networks
Machine
Learning,
Deep
Learning,
Neural
Networks Natural
Language
Processing,
Speech
Recognition,
Text-to-
Speech
Natural
Language
Processing,
Speech
Recognition,
Text-to-
Speech
Machine
Reasoning,
Decision
Making &
Algorithms
Machine
Reasoning,
Decision
Making &
Algorithms
Computer
Vision &
Imaging
Tech
Computer
Vision &
Imaging
Tech
"Core AI Technology"
Domains Come
Together to Deliver
Useful Solutions
Existing Usage of AI in Other Industries
Email SPAM FilteringImage Recognition Fraud Detection
Polling Question 1
Q:
Which area is a top focus for leveraging AI capabilities in
your organization currently?
A:
1. Protocol Development
2. Data Capture
3. Data Processing and Cleaning
4. ETL and Standardization
5. Data Analysis
6. N/A: None or Don’t Know
Polling Question 2
Q:
Which area do you think has the most potential value for
leveraging AI in the near future?
A:
1. Protocol Development
2. Data Capture
3. Data Processing and Cleaning
4. ETL and Standardization
5. Data Analysis
6. N/A: None or Don’t Know
Panel Discussion: AI Use Cases for DMAI Opportunities, Approach, Challenges and Predictions
Use Case 1 – Reducing Time to Database Lock by Automating Query Generation
EDC
Site Investigators
Case Report Forms (CRFs)
Data Managers
Data review
Queries to sites
1
2
3
4
Algorithms can learn from historical query data patterns and apply this knowledge to new studies
Predict required queries with a high degree of accuracy based on training data and/or programmable rules
Bots can be created to automate query generation if prediction meets acceptable thresholds
Opportunity
Use Case 2: Protocol Design
Problem on-hand:
- Inefficient patient selection and recruiting techniques are some of the main causes for trial failures
- The complexity of study designs is magnified by the difficulty of identifying the right patient population, inclusion and exclusion criteria, sample size, etc.
Opportunity:
- AI and Machine Learning algorithms can better utilize observational, safety and historical data to identify the right patient population, inclusion and exclusion criteria, etc.
Use Case 3: Data Mapping
Problem on-hand:
- The increasing complexity of clinical studies means more data collection and analysis
- Numerous data collection systems and many different structures and formats
- To get the most value and insights out of clinical data, it must be mapped/standardized
- Data mapping and standardization are time consuming tasks that rely on identifying tables, variables and code-lists to apply proper transformations
Opportunity:
- Using AI and Machine Learning, algorithms can scan through source data, identify it and apply the right transformations (auto-mapping) or suggests the highest matching transformation (guided-mapping)
Use Case - Neural Networks Identifying Source DataPAT_ID PAT_B_Date PAT_Sex Test Value
A-001 2/15/1971 M ALT 16.0
A-002 4/3/1963 F ALP 67.0
B-201 5/9/1972 F ALT 23.0
C-018 12/27/1959 M CK 102.0
Labs_2018_05_01.csv
Sample Data
Min Value
Max Value
Average
Field Label
Form Label
Characters Data Type In Range/List Labeled Contained
USUBJID
DOB
SEX
LBTESTCD
LBSTRESN
Use Case - Neural Networks Identifying Source Data
Sample Data
PAT_IDM PAT_B_Date PAT_Sex Test Value
A-001 2/15/1971 M ALT 16.0
A-002 4/3/1963 F ALP 67.0
B-201 5/9/1972 F ALT 23.0
C-018 12/27/1959 M CK 102.0
Labs_2018_05_01.csv
Min Value
Max Value
Average
Field Label
Form Label
Characters Data Type In Range/List Labeled Contained
USUBJID
DOB
SEX
LBTESTCD
LBSTRESN
RecommendationsSetting the Foundation for AI
Technology and Data Hub Framework for Data Sciences
Metadata, Security & Governance
Integration Tier
Framework for data movement in all
formats and in all frequencies:
streaming or batch
Real-time
Master Data Management
ConsumptionTier
Dashboards and Visualizations
AI/ML model inference APIs
Data-driven web applications
Automated systems and bots
Mobile devices
Data Sources
Structured
Semi-structured
Unstructured
Sensors
Wearables
Services TierDeploy and run distributed applications or services for interoperability
Processing Tier
Analytic workspaces to develop data pipelines, package and train AI/ML
models
Metadata Management, Security & Governance
Storage TierCollection and storage of structured and unstructured data on accessible infrastructure
Execution Tier
Execution of complex workloads on massive and variant data for advance
decision systems and analytics