Explains how Cancer Management can be made more effective using an integrated approach. From collecting data across reports to predict tumor growth and to be able to help users manage their condition through diet or therapy, the platform can be used to constantly track and monitor outcomes.

1. Quahog Life Sciences
Cancer
Management
Platform Introduction

2. Outline Platform Overview
Cancer Scenario
Detection
Insights
Management
Collaboration

3. Platform Overview

4. ➔ Quahog's AI based Healthcare Decision System is built to take the accuracy of healthcare decisions
to a higher level using an explicit model
➔ The platform is designed for monitoring , extracting and generating outputs in real-time, reducing
human intervention
➔ The platform allows the user to access highly accurate insights as well as remedial recommendation,
making patient care swift and dependable
Overview

5. Information architecture
➔ The important feature of the Quahog Platform is its
explicit model, unlike traditional practice of implicit
modeling. This enables analytical users to conduct
designed experiments rather than random
observational studies
➔ Designed using the knowledge base of systems biology,
this explicit model draws relationship from a
bottom-to-top approach. It is distinct from other models
that it links omics data right up to the symptoms
➔ The uniqueness of the design is that the –omics data
layer is linked to the pathway data, mapped to the
internal processes of the cell, close looping every
internal cellular processes
Explicit Model

6. How it works
The platform uses an approach that is similar to generative adversarial network (GAN), in the sense that the
expert system acts as the adversarial network and the unified patient application acts as the generative network.

7. ML Pipeline

8. ➔ Confounding Variables - The confounding variables are declared explicitly
using subject area knowledge, so there are no gaps in analysis, which could
lead to inaccurate results
➔ Standardized Regression Coefficients - Larger coefficients don’t
necessarily identify more important predictor variables. The platform
standardizes the regression coefficients so they’re based on the same
scale, allowing in comparison
➔ Run-time Computation - Dynamic Scoring algorithms and Markov
Decision processes help in real-time computation and insights
➔ Last mile Insights Delivery - Insights can be delivered to patient app for
diet, drug or therapy related recommendation
➔ Continuous Learning - As new data gets collected, new unique patterns
are extracted, which allows in progressive learning within the system
Benefits

9. Cancer Scenario
➔ Using the model, we could extract patterns that lead to various tumor formations, which are among
many types of mutations a cell can undergo. In our study, it was evident that for a cell to convert to a
mass of defective cells (tumor cells), it is critical that to identify if the tumour suppressor gene is
underexpressed in a damaged cell

10. ➔ Cancer is a disease of cellular mutation, which means the possibility of mutation can happen among
200+ unique cell types, causing their own cancer type
➔ The problem with cancer is its distinct feature of forming tumors. A single cancer cell might not
pose so much of a problem unlike a colony (tumor). Single cancer (mutated) cell is cleaned up by
body processes
➔ It is important to understand the severity of the disease by way of discovering the pathways that
promotes growth in cancer cells leading to tumor growth
➔ The pathways have more to do with replication cycle of a cell and do not associate with production
or digestion processes of the cell
Our study

11. The cycle where a damaged cell is formed

12. Start-Stop processes

13. Expression States

14. 3 possible scenarios

15. https://hms.harvard.edu/news/super-suppressor
Clinical
Proof
For the first time,
researchers have shown in
preclinical models that it’s
possible to treat cancer by
delivering a gene that
naturally suppresses
tumors.

16. Detection
➔ Know that we have stated explicitly what the primary detection features, it would require us to
collect data that reveal features that lead to tumour formation

17. ➔ Symptoms (Explicit collection)
➔ Lab Reports (Path) - CBC, Blood Protein Testing, Tumor marker tests, Circulating Cell tests
➔ Lab Reports (Radio) - Image Recognition from PET, CT, MRI, Endoscopy
➔ Lab Reports (Biopsy) collecting data on tumor Stage and Grade
Integrating Data for Holistic Detection

18. Measuring membrane potential
Cancer cells exhibit both lower electrical membrane potentials and lower electrical impedance than
normal cells (Cone, 1985; Blad and Baldetorp, 1996; Stern, 1999). According to Cone two of the most
outstanding electrical features of cancer cells is that they constantly maintain their membrane potential
at a low value and their intracellular concentration of sodium at a high concentration.
The cell membrane potential of cancer cells are about -15mV to -30mV. Exposure of cancer cells to SPMF
will normalize this potential, thereby halting the process of cell proliferation. A cascade of effects follows
normalization of cell membrane potential, i.e. increased influx of Calcium, Potassium ions and Magnesium
and efflux of Na & H2O out of cells, and reduction in intracellular acidity.

19. Insights
➔ Generating Diagnostic and Predictive results for preventive measures

20. Insights
➔ Stage based Insights
◆ Diagnostic Insights
◆ Predicting Tumor Growth over time
◆ Predicting Tumor Growth in space
➔ Grade based Insights
◆ Predicting structural differentiation (Well differentiated to undifferentiated)
➔ Generic Insights
◆ Predicting p values for data from symptoms and reports

21. Management
➔ Constant monitoring to ensure timely addressal and to prevent complications

22. Constant Management
➔ Nutrition Monitoring - The Mobile App monitors regular food intake and recommends diet that is
best suited for the patient. In return, the machine learns from the responses to the patterns.
➔ Key Tests Monitoring - Feature detection from specific blood/urine home tests or periodical
consultation and reports are collected from the patient for ongoing analysis
➔ Therapy Recommendation- Based on rules or past learning, propose therapy intensity, frequency
or other patient care that allows for prevention
➔ In-App Chat/Messaging - Allow users to chat with designated doctors to get their queries
answered or get suggestions to any unexpected ailments

23. Collaboration
➔ Working together to create a effective cancer management system

24. Service Areas
➔ Setup Model with available data and reference it with our explicit data to highlight missing data
parameters
➔ Allow them to integrate data sets to create unified record of patient for space/time analysis of the
patient
➔ Drag and Drop Interface to create visualization or slice/dice insight data
➔ Streaming pipelines for runtime insights and recommendations
➔ Dedicated data team to handle data organization, algorithm customization and tech
implementation

25. Thank You
Quahog Life Sciences
www.quahoglife.com