QuahogLife | Solutions and Services

Quahog Life Sciences | Solutions Overview
AI & Analytics
For Enterprise Healthcare
SOLUTIONS & SERVICES

DATA MANAGEMENT
Data Architecture and Organization

DATA MANAGEMENT
Platform Capabilities
Logical Data Model
Model designed for RNN
The platform’s fundamental capability is
to unify data sources and create a single
data store for patient data.
The platform allows the user to import
data using connectors, which can be
merged/joined using a simple editor.
Multiple data sources can be merged to a
single structured file, which can be used
for machine learning or analysis.
The data structure is designed using
the concepts of systems biology where
each unique entity or data type is
extracted for available relationships
from –omics databases. The data
structure is hierarchical with semantic
relationships
The user can create their own schema or
use the prebuilt universal schema to map
data columns and edit the relationships,
if necessary
The inbuilt learning module constantly
extracts relationship patterns between
two entities, once it is published by the
user
The model is suitable for back
propagation through structure, which
is necessary for temporal analysis and
predictions based on historical patient
data

DATA MANAGEMENT
For Patient-Centric Streams
The primary output of the platform is to
organize data by patients. Patient data
can be merged using a common identifier
or by inserting a common ID to all
sources collecting patient information.
Individual patient data is stacked
sequentially using the time stamp to
create a event map of a single patient,
allowing in learning health parameters
from historical data with respect to time.
These patient streams can be connected
to any models or applications, allowing
consumption of patient related insights
on any application

DATA SECURITY
Advanced Encryption
The platform offers advanced data security features, which uses a encryption
technique that rehashes all the given data into jumbled text. The verifying
algorithm has the key to decipher the text. The multi-state shuffle has a inbuilt
random function, which activates whenever a new record is created. Using
conditions, the algorithm encrypts, reading from 12 different instructions
The technique is similar to methods like zero knowledge proofs, which provides
enhanced privacy for its users.
It makes it easy to share data not only between doctors but also across learning
platforms without revealing who the patient is.

DATA ANALYSIS
Machine Learning and Analytics

DATA ANALYSIS
Platform Capabilities
Scoring Based Segmentation
Keyword based Segmentation
The platform has an inbuilt learning
model which is co-ordinated by several
runtime analytical models. For Analytics
purpose, users can custom define
independent models that suit their
analysis in order to get custom analytics.
The environment allows in integration
with external models from R, Matlab,
Weka and others
By assigning scores to unique column
types, users can create hierarchical
scoring patterns, which allows in
condition based scoring in order to
arrive at a aggregate score for a
particular entity. Useful for behavior
scoring and segmentation.
For data drill down and segmentation, the
platform offers an interface to search
data or create basic data segments.
The platform also offers conditional
segmentation which can be achieved
either by ranking or by keywords
Using keyword extraction, users can
create segments based on named
entities. This works on text coming
from doctors prescription or
symptoms data or other health
triggers, and groups patients by a
particular trigger keyword

DATA ANALYSIS
Models Supported
Behavioral Pattern Extraction Collaborative Filtering
Medical NERProbabilistic Attribution
Behavior Patterns can be extracted from
patient interaction data, cellular
interaction data as well as molecular
interactions
Useful for making automatic predictions.
Users can predict the behavior of a certain
compound, based on past patterns of
similar compounds
Useful for deriving the probability of a
certain event by ranking interactions of
all influencers. Can be used to find
influencers in a certain pathway or
process
Useful for information extraction. NER can
be used to quickly detect or tag textual
information by looking up pre- defined
data sets like NCBI Data Sets

USE CASE | PATTERN DISCOVERY
For Cancer Research
The assignment was to find patterns in behavior of
onco gene and tumor suppressor gene and to
understand the possible combination that led to
tumor formation
Results
Analysis clearly showed that not all cells, that
escapes the Apoptosis cycle can lead to tumor
growth. Only cells where the growth promoter
gene is overexpressed and the tumor suppressor
gene is underexpressed

USE CASE | INFLUENCER DETECTION
For Cellular Research
The assignment was mostly internal where the
challenge was to extract possible inputs from
aggregated outputs in order to understand the
least unique factors that influence cell death.
Purpose
This was done to understand the negative
influencers that cause cell death, which can be
useful in preventing cell death or cellular ageing
Method
By extracting all forms of cell death to its
influencing process, and by further extracting
them to the constituents of the process, we were
able to apply probabilistic attribution to
understand the influence of the constituents on
the process and its related type of cell death

DATA ANALYSIS
Learning Workflow
The diagram illustrates how data is organized for machine learning workflows and how knowledge
extracted is delivered to the end user application

DATA ANALYSIS
ML Differentiators
Recursive Neural Network (RNN)
Using this variant of RNN, the
platform can be used for back
propagation through a hierarchical
structure. The structure is defined by
integrating all the unique
parameters available in the scope of
systems biology
The biggest strength of Quahog’s model for machine learning is that it is capable of doing both
space and time based analysis
Cell-Centric Structure
Using the cell as the central node,
it is possible to extract top-down
patterns for diseases and bottom-
up patterns for associated
molecular behavior

DATA VISUALIZATION
Interactive Dashboards

DATA VISUALIZATION
Insights & Data Visualization
Multiple Charts and Graphs
Users can select their reports and display
them using multiple charts ranging from
time series line charts, to areas, pie, line,
scatter/bubble, to combinational charts
with dynamic interactivity
Dashboard Templates
Users can create their own dashboard
layouts or select from the theme library,
enabling different presentation styles
and themes
The platform provides users with data visualization and auto configurable insight generation, which
has been made simpler with couple of steps, before users can generate dashboards of their own and
share with their peers. Key Features are
Shareable and Embeddable Formats
The dashboards can be easily shared by
assigning email addresses and can also be
shared as ppt/pdf templates. Users can also
copy the embed script and paste it to any
web application for insight distribution
Real-time Monitors
It can be customized with schedulers to sync
data and provide dashboards with real-time
data visualization

DATA VISUALIZATION
Interactive Dashboards
The platform lets users create multiple dashboards with interactive charts, using a simple intuitive
editor, in 2-3 steps

PLATFORM –ADD ONs
Applications

BOT ASSISTANCE
For Diabetologists
For the number of people suffering from Diabetes, with the available ratio of doctors and their
time, it becomes extremely important that all patients suffering learn to manage the self with
respect to awareness, prevention, and diagnostic assistance. This helps the crop of doctors to
advise more patients and focus more on the right treatment.
Bots are trained on master diabetic data and can
assist patients with diabetes related queries, and in
turn, update the concerned doctor as to whether
any attention or immediate treatment is required.
Based on the symptoms reported, bots can
recommend relevant tests, and based on these test
results can help detect a probable Diabetic or a
more specific pre-diabetic scenario.
Doctors can use these bots as their personal ‘front
desk service’ or as kiosks and thereby offer better
management of the available doctor’s and patient’s
time.
Background
The Bot application

BOT ASSISTANCE
For Physicians
The solution emphasizes the centrality of the role of physicians in utilizing AI as a tool to
supplement their decisions as they provide patient-oriented care. Physicians can not only ease up
time and address more patients, but also can depend on the system for deeper assistance and
highly accurate diagnostic suggestions.
Using symptoms and diseases data, bots are
trained to detect the probability of diseases based
on symptoms. Relationships between 1104 unique
symptoms is mapped to ICD database, using
probabilistic attribution, in order to detect the
most probable conditions
Physicians can use these bots on their desktops and
can feed the patient's symptoms to get a list of all
probable associated conditions. It can even prompt
physicians to check on symptoms that might not be
collected from the patient.
Background
The Bot application

Quahog Life Sciences Pvt. Ltd.
53/A, Dollars Colony, 2nd Main Road
J.P.Nagar 4th Phase
Bangalore 560078, India
veer@quahoglife.com

QuahogLife | Solutions and Services

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