Artificial intelligence in Pharma by Malai Sankarasubbu

Copyright © 2019, Saama Technologies
Delivering Analytics Advantage
to the Global 2000
Artificial Intelligence in Pharma
Malai Sankarasubbu

2
AI is Everywhere

3
Advanced
< 5% Battery
No fare
shown

4
What about Work Life ?

5
Perception Understanding Reasoning
Artificial Intelligence

6
Conversational Experience

7

8
Can you talk to your
ClinOps Data ?

9

10
Open vs. Closed

11
Intent Entity
Domain
Data
DaLIA

12
NLU should be
Pharma Specific

13
Intent Extractor
Understand what the user means!
“What are the Protocols in severe risk ?
“
Intent: Portfolio Schedule Risk
Entity Extractor
Extract entities to make an action!
“What are the Protocols in severe risk
?”
Status: severe
Pharma-specific NLU Engine

14
Library of ClinOp Intents

15

16
How do We Wire it Together ?

17
• One question one answer system is not good CX.
• They should have ability to hold conversation.
• Users respond with please and thank you after 6 responses.
Context Driven

18
User Feedback

19
I am Confused, Can You Help Me ?

20
What is AutoML?
Robots taking over data scientists jobs…
“More than 70% of US fears robots taking over our lives, survey finds”
- The Guardian
Way to scale data science methodologies

21
Data Science is Expensive
Confidential
Saama Technologies Inc.

22
Foe?
VS.
Images Credit: sevendaysvt.com, atmtrader.com

23
But from a Different Perspective…
Image Credit: sevendaysvt.com
Crowd of individuals to be served
$60
Apply for
Mortgage
$40
Deposit $60 $100
Money
Order
$20

24
Adoption of ATMs didn’t Decrease Bank Teller Jobs
“Learning By Doing” James Bessen
Tellers
ATMs

25
Remove malformed samples,
account for missing data, feature
selection, and other methods of
making data consumable by the
model
Pre-Process Data
Train a multiple models on the data
over multiple iterations to build a
reliable model with high accuracy
Build Models
Run the model on test data and redo
building until model it performs
satisfactorly on the test data
Test and Validate Models
Leveraging Your Own Data
Processing, cleaning, deriving insights

26
Introducing AutoML
Using AutoML is as simple as pointing the engine
to the database and specifying the target
There are no extra steps involved!
Raw Data Deployable
Model
AutoML Engine

27
EASY VALIDATION
OF IDEAS
A short turnaround time enables
trying out multiple ideas so that no
insight or opportunity is missed
CUSTOMIZEABLE
WHEN NESSESARY
Easy to use interface and clever
algorithms enable even the most
inexperienced user to build good
models
LEVERAGE ML WITH
SME’s
An experienced user has the power to
direct the model building using insights
from his/her area of expertise
SHORT IDEA TO
DEPLOYMENT TIME
Short training times and fully
deployable code enables quick
deployment of models
What Does AutoML Enable?

28
How Does it Do That?
Imputations tailored to
your data
Smart
Imputations
Automatic feature selection
for a better model
Automatic Feature
Engineering
Simultaneous training and
evaluation of multiple models
Model Training To know what we don’t know
Model
Interpretability
For easy deployment of model
Auto-Generation of
Pipelines Fully functional scalable
code
Deployable Model
1
2
3
4
5
6

29
Augmented Intelligence and Machine Learning
Image Credit: mediaupdate.co.za
1. Use AutoML for exhaustive, efficient data prep, model generation and
ensemble generation
2. Apply model explainability
– Debugging
– Inform feature engineering
– Direct future data collection
– Build trust

30
Illustration: Predicting Clinical Trial Completion Success
Feature Selection and
Model Generation
Handling of Missing Data

31
Illustration: Predicting Clinical Trial Completion Success
Output with Explanation
Human check:
– Does the explanation make
sense?
– Are there features missing?
– Is there another question to be
asking?

32
An Attentive Sequence Model
for Adverse Drug Event
Extraction from Biomedical
Text
Differential Privacy
PHI Scrubber
Interpretability
MedNLI
Parking Lot Detection
Parking lot Occupancy Detection
Saama’s Papers Published on AI for Pharma

Artificial intelligence in Pharma by Malai Sankarasubbu

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