Machine learning is one of the most prominent components of technology and development. See how cognitive calibration accelerates machine learning along with the benefits, challenges, and products.
Kalpesh Balar, Coseer
3. EXAMPLES
• Building healthcare product database
from 35m documents
• Compiling 3m documents every day into
actionable stock insights
• Reading through complex legal/ technical
documents to answer questions
2
15. PROBABILISTIC CLASSIFIER
f(x) p(R)
• Probabilistic distribution of the tier
• f(x) can be machine learnt independently e.g.
political correctness of statements
14
17. CLOSED LOOP CLASSIFIER
F(RO) F( f(x) p(R) )
• Probability distribution of a Probabilistic Classifier
is improved by results of the main machine
• Closed Loop Classifier Deterministic Classifier
16
20. PRODUCT DATABASE
Client
Problem
19
• One of the largest healthcare Companies
• 10m+ SKUs
• No standardized database comparing
attributes of products
• Previous human attempts to build
database unsuccessful
21. INPUT
• Product Brochures
• White Papers
• Surgical Protocols
• Sporadic human
entries by previous
attempts
20
Drill Bit 4.5 mm Cannulated Jacobs Chuck
With 135 mm Stop 165 mm Humeral Nail-
EX Instrument 03.010.089 03.010.089
DRILLBIT 2.0 MM GUIDE WIRE 4.5MM
CANNULATED DRILL BIT JC/WITH 135MM
STOP/165MM 45mm cann BIT BIT DRILL
03.010.089* BIT DRILL CANN 4.5MM X
165MM BIT DRL CANN 4.5X135MM BIT
DRL CNLD 4.5X165MM DRILL Drill Bit 4.5
mm Cnltd Jacobs Chuck 135mm…
22. CHALLENGES
• Erroneous data e.g. human entries
• Different levels of detail e.g. Metal, CoCr, CoCr46
• Extensive use of context specific shorthand e.g.
OD = outer diameter for acetabular shells,
OD = optical density for intraocular lenses
• Incomplete or missing data
21
23. MANAGING CONFLICTING DATA
• Sources tiered based on accuracy and specificity
Product Brochures
White Papers/ Surgical Protocols
Authenticated Web Data
Human entries in the system
Non-authenticated web and other data
22
24. IDENTIFYING MATERIALS
• Confusion of materials of the part itself and
corresponding parts.
– e.g. “Coated Tube Poly Silicone”
– Unclear if coating is Poly or Tube is Poly
• Constrained solution spaces using Deterministic
Classifiers help
– e.g. “parts use plastic tubes” tubes cannot be silicone.
23
25. FIGURING OUT SHORT HANDS
24
Probabilistic
Classifier
• Classifies docs if
other info identifies
part category
• Trainer uses high
reliability data to
identify more
classifying features
Primary
Trainer
Reliability: High
All corpus
“outer diameter”
“optical density”
Continuous Learning
“outer diameter”
“optical density”
Reliability: High
Reliability: Low
“outer diameter”
“optical density”
26. OUTPUT
• Learnt attributes and their
corresponding values for all parts
• Impossible without multiple
cognitive calibration frameworks
deployed in models
• Difficult to implement without
other strengths in tactical
cognitive computing
25
Category
Sub-Category
Diameter
Stop
Length
Guide Wire Dia
Cannulated
Radiolucent
Coupling
Platform
Reusable
Drill Bit
Humeral Nail
4.5 mm
135.0 mm
165.0 mm
2.0 mm
Yes
No
Jacobs Chuck
Synthes (Estd.)
Yes
27. T H A N K S
kalpesh@coseer.com
www.coseer.com
26