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