An AI-based Decision Platform built using unified data model, incorporating systems biology topics for unit analysis using semi-supervised learning models

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Unified Medical Data Platform for Highly
Accurate Diagnosis, Predictions and
Prescriptions
An AI-based Decision Platform built using unified data model, incorporating systems
biology topics for unit analysis using semi-supervised learning models
Authors
Veerendra, Dr. Kannan Mavila, Dr. Arjun Rajagopalan, Frank Boehm, Margaret Morris,
©​ ​March 15th, 2018​, ​Quahog Life Sciences Pvt. Ltd., Bangalore, India
Quahog Life Sciences Pvt. Ltd., Bangalore, India

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Contents
Introduction 2
QuahogLife Integrated Environment 4
1. Expert System 6
1a. Conceptual Data Model 6
1b. Machine Learning 10
3. Data Collaboration 13
Applications 13
Personalized Medicine 14
Machine Learning Use cases 15
Recommendations for Preventive Care 16
Summary 18
About Quahog Life Sciences 19

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Introduction
Today's healthcare decisions are not completely foolproof in that they can lead to fatal errors.
Statistics show that an estimated 850,000 ​medical errors​ occur each year, costing over £2
billion. Each year in the U.S., approximately 12 million adults who seek outpatient medical care
are misdiagnosed, according to a ​new study published​. The third most deadly killers of
Americans are medical errors, accounting for more than 250,000 deaths each year, according to
the ​analysis​. These medical errors arising from incomplete or inaccurate analysis could have
easily been prevented. Therefore, it is critical to understand why misdiagnoses occur; and the
problem requires careful evaluation of diagnostic systems and processes. Uncovering and
remediating flaws in existing techniques can greatly reduce the risks associated with
misdiagnoses.
Erroneous healthcare decisions often result from the lack of data in relevant areas, due to
compartmentalization stemming from patient data lying in silos and historical data not being
available for analysis by qualified diagnosticians. In other words, the problem involves data
compartmentalization and/or for the failure of data to be proactively shared with those in the
best position to make the best use of it.​ This fosters a pattern of highly assumptive decisions
and a high potential for erroneous heuristic analysis.
The abundance of wearables and other devices capable of collecting diagnostic data should
reduce the risk of such oversights. Although this type of data increase should be favorable for
analyzing patient data, it also adds additional silos, and with them hurdles for realizing this
opportunity. The problems are further complicated by the lack of universal data tagging
between sources and precise definitions for what is being collected. Re-orchestrating the
collection and categorization of patient data to optimize visibility and availability of such data is
an opportunity that must not be overlooked. It would facilitate the ability to analyze patient data
holistically. Preadministration of data tagging could reduce compatibility issues in measuring
and categorizing such data. Siloed data could then be merged from their source and tagged
automatically without requiring and manual post processing.
An important goal of next-generation health care is to provide a platform to ensure that every
user is ​up-to-date​ of his or her bodily functions so that they may be alerted to deviations. This
would minimize the negative impact of prolonged neglect and support the agile restoration of

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normalcy. Providing meaningful alerts and recommendations for even the smallest possible
detectable deviation furthers the benefits. For example, nano-trackers capable of real-time
analysis of blood, urine and saliva may be able to predict and recommend dietary or
pharmaceutical measures prior to the development of debilitating patient symptoms, allowing
patients to self-correct. Such early detection and resolution may be used to clear the patient’s
system of defects; the accumulation of such defects is hypothesized to be the main reason for
ageing. Quick external resolution also reduces the load on the immune system, thereby
increasing the patients’ potential longevity.
Advancing health care to meet this goal requires AI-assisted decision systems that are capable
of detecting patterns (by performing health diagnostics) and providing recommendations
(based on additional testing, habit transformation, treatments, etc.) quickly and accurately from
the data of home medical devices, test results and other data sources.
Seamless data flows within such a system and its integrated sources will allow for machine
learning to uncover the myriad patterns derived from a multitude of patients. This presents an
opportunity to use deep learning to develop unparalleled medical expertise within an intelligent
system. In order to rapidly make precise medical decisions, such a system will require two
major components:
1. A Patient Master or single version of truth (SVOT) for patient data. This greatly
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facilitates the creation of patient-centric data stacks which include historical patient
data collected across multiple devices, test results, prescription regiments, treatment
history and the like. Robust patient-centric data enables holistic analysis required to
increase detection and prediction potential of the second component.
2. An Expert System – A decision engine capable of outputting every possible pathway or
process within the human body. When patient data is recorded, it is stored within the
patient’s data stack (as a pattern) that is passed to this Expert system for pattern
matching and deviation detection. The Expert system then uses the resulting patterns
and deviations to determine cause and output one or more solutions to return the
pattern to equilibrium (i.e. a treatment recommendation).
Notice that the first component is analogous to the patient profile as kept by a traditional
doctor’s office, except that the profile is enhanced by data merged from every type of doctor
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​Single version of truth or SVOT is a technical concept in computerized management essentially meaning
a single Centralized Database.

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that the patient may visit, and it also contains the results of every test taken by the patient and is
being continuously augmented with updated data from patient borne sensors. This greatly
enriched patient profile can dramatically reduce misdiagnosis due to lacking or siloed data. The
second component is analogous of the feedback loop between the patient and the doctor
except that it is not limited to the frequency and duration of traditional doctor appointments.
Nor is it necessarily reliant on the patient to first detect the symptom, then seek diagnostics.
This type of feedback loop can greatly reduce misdiagnoses due to poor, infrequent and/or lack
of patient-doctor communication. It is also likely that the combination of early detection and
continuous monitoring of treatments and responses will reduce the severity of disorders, the
need to resort to drastic treatments ​and the risk of a mistreatment and its progression.
Developing a system as described above would address issues in the collection, consolidation,
and categorization of patient data and the flow of that data to the experts and systems in the
best position to make use of it. Using it for deep learning related to homeostasis and its
deviations creates opportunities to advance the speed and accuracy of current diagnostic and
prescriptive capabilities. This can provide for more accurate and earlier diagnoses that can
often prevent illnesses, and it affords more accurately selected treatments and more speed in
the detection of treatment complications.
QuahogLife Integrated Environment
The QuahogLife Platform manages life-long patient data. Managing patient data involves far
more than just the unified storage of patient data collected and united from all available
sources. It also requires outputting individual health patterns, effects, insights and other
knowledge that is useful for predicting and prescribing effective remedies. In order to facilitate
the required data transformation and otherwise achieve these objectives, the integrated
platform houses three core modules that operate in tandem: an Expert System (ExS), a Unified
Patient Application (UPA) and Data Connectors.
The graphic below illustrates the integration of the three modules along with the data flow.
(1) The Data Connectors collect data either from medical devices (analogous to pathology
laboratories). Connectors for devices, including mobile apps, nano-trackers and other cellular
testing methods, push data to the Unified Patient Application module (2) for organization. The

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organized stack is then passed to the Expert System module (3) for learning and inference. ​Its
outputs are collected back to the Unified Patient Application to update patient records.
Data are organized by patient (each of whom is assigned a unique ID), and pushed to the Expert
System where it will be analyzed for deviations from the healthy patterns established earlier.
Detected deviations (useful data that contribute to the model) trigger the diagnostic and
treatment process. Feedback from treatment monitoring is also made available to the system to
refine both the treatment schedule and the overall model. ​QuahogLife is able to continuously
improve its ability to detect and remediate abnormalities at large and predict specific responses
of individuals while ensuring robust and up-to-date patient profiles are kept.
The mechanics are cyclical. ​Data are continuously being collected, processed and analyzed to
discover patterns.​ ​The patterns are also characterized as being indicative of one’s state of
health, it’s improvements or areas of deterioration and many other key indicators.​ This new and
useful information mined from raw data is called insights. ​Pattern recognition and probabilistic
attribution applied to patterns, their deviations and developed insights account for decisioning
related to detection and diagnosis.​ ​Much or this process is managed by the Expert system
component, described in greater detail below.

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1. Expert System
The learning and decisioning aspects of the Quahog Life Science Platform resides in its Expert
system. This module houses all of the necessary parameters for the optimal functionality of the
human body. For example, the Expert system stores information on optimal blood pressure, and
maintains a knowledge base of millions of blood pressure reading patterns seeded by past
research and augmented by new learning. It holds optimal range data for every pattern,
including anomalous patterns discovered in healthy, long-lived people.
When paired with patient date from the Unified Patient Application, our Expert system emulates
doctor-patient interactions wherein the patient provides input about symptoms and the doctor
orders tests, the results of which are used by the doctor to determine a course of action or
prescribe a remedy. The Expert System can be compared to the doctor, in that the system has
the data (knowledge) relative to performance ranges for each potential pathway, which can
readily detect abnormal values while comparing inputs from the Unified Patient Stack (the latter
of which can be compared to the patient).
The Expert System employs a hierarchical unified schema as its learning network. The schema
design facilitates pattern detection and recommendation selection for remediation at great
speeds and with great accuracy. The logic behind the data model that make this possible is
described in the section below.
1.1 Conceptual Data Model
The Quahog unified data model incorporates the scientific principle of Systems Biology to
accomplish unit analysis that utilizes semi-supervised learning models. The following is an
extract of the way the Institute for Systems Biology, in Seattle, summarizes Systems Biology:
It is a holistic approach to deciphering the complexity of biological systems that starts from the
understanding that the networks that form the whole of living organisms are more than the
sum of their parts. It is collaborative, integrating many scientific disciplines – biology,
computer science, engineering, bioinformatics, physics and others – to predict how these
systems change over time and under varying conditions, and to develop solutions to the
world’s most pressing health and environmental issues.

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https://www.systemsbiology.org/about/what-is-systems-biology/
In other words, the conceptual data model used in the Expert system is designed using the
concepts of systems biology. As in chemical biology, changes in the micro patterns influence
macro patterns, making it important to build a relationship graph of molecules to aid analysis.
This graph architecture allows users to cluster molecules based either by their properties,
functions or responses by sorting relationships between the molecules.
Hence, to model an entire cell, multiple molecular pathways must be integrated to order to
analyze the macro causes and effects that cell can undergo or express. To model a particular
cell, we designed a conceptual data model in which the cell is classified by its functional units.
Relationships between these functional units were extracted based on their involvement in all
pathways cataloged by the KEGG Pathway Database . In this way, a new database of all the 2
unique entities and their pathway relationships was developed to facilitate the visualization of
the spatial and temporal dynamics of both receptors and the components during signaling and
activation.
This network was enhanced by connecting other important databases (including, but not limited
to, genomics, epigenomics, transcriptome, protein, and all others) to better understand the
influence of such components on signaling pathways. This integrated network will assist with
visualizing any/all patterns and parameters that influence changes in a specific pathway from
multiple dimensions. An example is visualizing the influence of cellular metabolites in signaling
and epigenetic regulation, and/or the initiation of TF-driven gene expression.
This network of unique cellular components and their definitions form a unique cell. Over 200
such unique cell nodes have been created, forming a layer with cell definitions based on
variations of their properties and functionalities. This cellular layer forms a tissue layer, which
forms an organ layer, with relationships to the 13 distinct macro functions of the human body.
Traditional analysis is top-to-bottom, starting with macro factors such as symptoms that can be
linked to a certain disease that may affects a particular organ. The bottom-to-top approach,
described herein, goes beyond diagnoses involving symptoms indicative of specific ailments by
uncovering the underlying relationships between those organs, their components and their
collective mechanics of the underlying systems. With the integration of a disease database, this
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​KEGG​ Pathway Database – ​Kyoto Encyclopedia of Genes and Genomes​ Pathway Database

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methodology can extract the most probable cause of a disease at its lowest levels. The diagram
below shows the network for how different topics are connected at different layers.

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The graphic from KEGG shows the linkages between various metabolic pathways.

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1.2 Machine Learning
Building the underlying network described above is essential, and the network is complex.
However, machine learning techniques designed to measure similarities between related
objects are capable of doing so. New connections are created as dynamic relationships
extracted between similar labels or keywords are uncovered by the learner. For example, using
an exact keyword match, relationships are automatically created for every unique label across
data sources, eliminating the risks of manual tagging and associated errors. This relationship
strength between the unique labels are updated with a certain weight every time an association
is detected in a unique pattern. Using a temporal sequence of activities within a signaling
routine, patterns are generated.
These patterns are then compared with user input patterns for matching, predicting scenarios,
analyzing individual patterns and even analyzing the unknown targets in a specific pattern, using
machine reasoning techniques. This simplifies the matter of identifying all associated
influences, and the process can be used to apply probabilistic attribution to understand the
various degrees of attribution for each influencing parameter. With patterns available for
reference, the learning engine can rapidly match input patterns and also learn from new
relationship patterns that were not previously recorded.
The knowledge base utilized by the Expert system is enriched by two learning processes. The
goal of the first is to acquire knowledge about optimal versus non-optimal molecular behaviors.
The goal of the other is to derive patterns from the raw data. As learning progresses, the Expert
systems ability to predict and prescribe more rapidly and accurately also increases.
Semi-supervised learning techniques are used for schema editing and target variable
configuration. Classification, clustering, auto-sequencing and matching are carried out with
unsupervised learning algorithms.
2. Unified Patient Application
The Unified Patient Application is the single version of truth, or patient master for the Quahog
Life Science Platform; collecting patient data from all available sources, optimizing its structure
and curating medical history. The patient data set acts as a memory file following the same

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schema of the Expert System, facilitating its ability to rapidly provide input as required and
receive new information (from the Expert System, etc.) to be recorded as part of a patient’s
updated medical history.
The rise and proliferation of connected devices includes those collecting health and fitness data
on patients. It can be expected that, over time, such data will become more readily available.
Data streaming just-in-time from wearables and medical devices, and the capture of digitized
reports, records and treatment schedules, are increasingly more valuable for producing holistic,
individualized medical histories.
Incorporating an individual's adherence, drifts or anomalies in global patterns into their record is
also important. That way, newly detected deviations may be extracted as key influencers of
particular patterns, characterized probabilistically, and further processed.​ ​Using the patient as
the primary parameter and its associated patterns, predictive patterns are then deduced along
with past remedial patterns, which may serve to provide for prescriptions or curative measures
tailored for the individual. Once the remedial measures are deployed through trackable drug
delivery or other means, the system can be integrated to capture the effect of the treatment so
as to trace how well these strategies worked. Such information enriches the patient master and
the knowledge base of the Expert system.
The continuous data flow allows the deep learning aspect of the system to record and learn new
patterns and consistently build successful patterns and store them in its memory, making this a
dynamic learning platform. The congruency of the data structures in the Expert system and the
Unified Patient Application facilitate pattern matching between both these systems. Based on
its past successful pattern matching, it generates the most effective and feasible remedy (or
choice of remedies), minimizing the likelihood of negative side effects.

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3. Data Collaboration and Security
The Quahog Platform will act as the centralized processing unit for all data analysis and
machine learning. For seamless analytical processing, data connectors will be available so that
administrators can plug in data from external devices either through API calls or through a
periodic scheduler.
The current system expects the user to personally upload every medical information from their
mobile application. For example, data collected as doctors' prescriptions, pathology/radiology
reports, and food/drink intake are all collected explicitly from the patient. The patient can also
sync the mobile app to external compatible devices to collect data implicitly.
Data collected are stored or stacked together as a document (MongoDB) based on the patient
profile information or app registration ID. The schema designed allows for instant mapping to
its respective entities, which makes data available for instant analysis or as inputs to the
learning module.
The outputs of the learning model can be accessed via an API call to either display it on an
application or as integrated to an automation system. This allows every application on the
Quahog Platform to deliver insights or recommendations in run-time (meaning that when the
data is collected, the platform can start to analyze and instantly generate outputs).
Patient data collected is secured using advanced cryptographic technology (called
'zero-knowledge proofs'), which requires patient authentication on the app to decrypt the file
pertaining to the app ID. As the data document is organized by user id, and each document
holds a unique key set, it is nearly impossible to get decrypt patient records​(the system
emulates blockchain techniques).
4 Applications
The Quahog Platform will act as a hub for all data silos, as data is transformed to create
individualized patient data and made available for downstream solutions analysis and machine
learning.
With advancing technologies, the resolution of medical-related issues is rapidly heading
towards far better disease control and elimination.​ ​New patterns and/or pattern data can be
obtained from the merging of output from nanotrackers (capable of capturing internal structure
and functional patterns) with output from individualized dietary and medicine reporting (external
data collection from wearables, test kits, and other sources).​ ​With the ever-increasing
abundance of recorded data, the need for a centralized unit becomes much more critical. With
innovative techniques of ​molecular manufacturing​,​ ​we are approaching a time wherein

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personalized medicines can be​ ​manufactured at home​. ​Owing to its self-learning and pattern
recognition capabilities, the Quahog Platform will be in a good position to upgrade in order to
provide for this, too.
Deep learning and pattern recognition are also critical for ​robotic surger​y applications, wherein
motion patterns are recorded and must be repeated by robotics with much finer accuracy than
conventional surgery can provide for. The personaized data the robotic surgeon records and
processes before operating will also facilitate more successful outcomes.
The illustration below shows how data collected from various point applications (apps or
wearables, etc.) can be ​federated​, organized and processed, creating a single environment for
informed personalized care:
At a high level, the platform can be employed for the following use cases:
(1) Personalized Medicine
(2) Machine Learning
(3) Recommendations for Preventive Care

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4.1 Personalized Medicine
With access to the unified patient stack, personalizing medicine and other forms of health care
become all the more feasible and simplified.​ Drugs and supplement doses​ can be based on the
specific requirements of an individual patient, unlike today's generalization of medicinal
substances. For example, if a standard dose of a medicine is too high for a particular patient, a
precisely effective dosage can be prescribed instead. In other words, the dose does not have to
be 50 mg (or any other standardized dose), but might instead be 39.5 mg, or some other
customized dosage that does not require approximations.
The Unified Patient Application would play a key role in Diet and Drug Personalization. To enable
the capacity to personalize diet recommendations or drug composition, analytical models
require holistic, individualized patient data in order to generate patient-centric outputs that offer
personalized solutions. The preorganized patient stack simplifies the process of applying
collaborative filtering or probabilistic attribution in order to recommend or predict outcomes and
deliver true personalized care.
What streamlines the process is that it collects data from sensors for a particular individual,
whereafter the system compares the dynamics of the input string to the global string (the
expected range within the human body) to determine whether the recommended substance and
dosage will have a positive patient outcome. ​Recommendations can be optimized to the point
where the composition of drugs, various herbs, vitamins or other nutrients can be approved
subsequent to measuring data concerning internal damage and the expected benefits.
4.2 Machine Learning Use Cases
The data organization within the data-stack allows for the rapid comparison of patterns. Pattern
Detection is one of the key aspects that will be applicable to many features of cellular studies.
This data organization creates the ideal circumstances for machine learning patterns within
cellular behavioral data and for learning motion patterns that are detected during surgical
procedures.

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● Pattern Learning in Cellular Behavior ​- Mastering, learning and analyzing molecular
behavior in cells requires a comprehensive understanding of the spatial and temporal
relationships of given molecules. The unified schema allows for the transformation of
data from various sensors to arrive at complete health patterns. The schema allows for
n-dimensional analysis, and this can provide for pivoting at any molecular node to
understand the cause and effect of a particular cellular behavior.
● Pattern Learning for Invasive Repair​ - The same pattern-learning technique can be used
to detect patterns in surgery processes, and to enable learning in robotic machines. With
learned predictions, robotic surgeries can be far more well planned out, with better
control of surgical procedures.
Full Pattern Detection, composed from various micro patterns, allows users to develop effective
repair strategies. Users can extract patterns across any dimension and visualize how and where
repair techniques can be implemented. Some of the examples are listed below
Example 1: ​Finding Unit Parameters that influence disruptions in cellular patterns leading to
cell death
Extracting full patterns to their unique unit
parameters allowed us to understand the key
areas of change that triggers a chain
reaction leading to degradation. The
hierarchy of events showed that all cellular
errors are caused during replication, and are
either directly or indirectly linked to gene
mutation largely due to oxidative stress.
These factors are responsible for either
physical cell damage or premature cell
senescence or a dysfunctional cell cycle
(causing autoimmune or cancerous states),
or programmed cell death (apoptosis and/or

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too much autophagy). These types of cellular damage lead to organ failure and finally to
death. ​Click here ​to see the full report
Example 2: Detecting possible combinations that lead to tumor formation
The inbuilt model helped us to 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 so that
a mass of defective cells (tumor cells)
formed, it is critical for growth promotion
genes to over express and for tumor suppressor genes to under express​.​ ​Click here​ to see
the full report.
4.3 Recommendations for Preventive Care
Although the body's repair systems are generally robust, there are times when they are
overwhelmed if left to their own devices. Although periodic health checkups are extremely
helpful, for most people, attaining and/or maintaining optimal health over a lifetime will require
tracking and monitoring processes that provide personalized care information and remedies
before the body is hindered by various types of damage, some of which are presently
irreversible or are characterized by very slow recovery periods.​It is medically established that a
factor​ promoting ​bodily aging is the accumulation of senescent cells.​ These cells not only lose
functionality, but they also release toxins to their neighboring cells, so that nearby healthy cells
can lose health stability sooner than they otherwise would. Tracking these inefficiencies and
administering a sure, rapid solution (such as drugs or a combination of nutritional supplements
and drugs) will be an expedient way to prevent the accumulation of senescent cells and their
toxins, to thereby slow down the aging processes.
Unfortunately, people tend to consult a doctor only when symptoms appear, and these
symptoms are typically the result of a process that has been mounting, often undetected, over a
period of time. Whereas, root causes can often be corrected by less aggressive means when

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detected early enough. Quahog’s Body Monitoring App assists individuals with monitoring and
remaining aware of their health issues, and allows them to take preventive measures when
alerted to a signal that indicates even a slight deterioration in a given health pattern. Users are
notified whenever there is a variant in one of their health patterns. Based on their pattern
analysis, users get instant recommendations to resolve basic issues that could be mitigated or
eliminated through a diet change or exercise routine. Users can share data with their doctors for
collaborative resolution. With steady-state, up-to-date information, users can take precautionary
measures and manage their health issues more effectively.
The Quahog app gathers data from several integrated sources and unifies it for holistic analysis.
The app receives data primarily from home testing devices (blood and urine), portable
ultrasound devices, prescribed MRI Scan inputs, along with other essential data from lab
reports. Although most periodic data derives from home kits, the user might have to visit a lab
occasionally for other inputs that are not collected from portable instruments. The app also
requests data concerning daily nutritional intake and exercise patterns; integrating them into the

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user's unified stack. Historical data, such as prescriptions taken and reports can be scanned
and uploaded into the app for patient data consolidation.
It is a convenient strategy to take advantage of various medical devices and wearables. Daily
checks on blood pressure, temperature, urine, blood and flow analysis, can yield very useful
information regarding one’s health status. For example, a simple ​urine test home kit​ can detect
abnormalities in urinary systems, kidney functions, liver and pancreatic functions, bacterial
infections, acidosis/sepsis, advanced kidney, bladder or prostate cancer, nutrition conditions,
dehydration and more. A handheld or ​wireless ultrasound probe​ can help detect the causes of
pain, swelling, infection, and diagnose heart conditions and even obstructions in blood flow.
The routine use of advanced nanomedical platforms, such as the VCSN (Vascular Cartographic
Scanning Nanodevice) conceptualized by NanoApps Medical, Inc., in Vancouver, Canada, or the
same company's Gastrointestinal Micro Scanning Device (GMSD), may, in the future, assist
medical science to elucidate processes and failures at the molecular level and administer
solutions to myriad health problems. Nanosensor-embedded wearables are being developed to
detect pathogens and provide measures that help to avoid early-stage infections.
Quahog makes it easy for users to always stay informed by connecting devices and obtaining
accurate medical decisions on their mobile/smart devices, keeping them up-to-date at all times.
For example, a feedback system invoking a urinalysis device reporting a positive ketone test,
combined with a glucose monitor reporting high blood sugar levels, will be smart enough to
advise a user to quickly drink plenty of water to flush away the ketones. Since untreated high
blood glucose with positive ketones can lead to a life-threatening condition called diabetic
ketoacidosis, it will be critical to test blood glucose every four hours and share data in real-time
with the assisting doctor to keep the user free of complications.
To manage and streamline the massive, ever-mounting health-related data, Quahog Life
Sciences is developing a decision platform that allows users to connect devices and receive
informed decisions on their mobile/smart devices, to keep users informed at all times.

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Summary
Quahog’s Unified Approach of bringing data together can make an enormous positive impact on
the healthcare economy through preventive medicine. ​Patients will have less out-of-pocket
expenses, avoid lost work time, and the costs of serious diseases and chronic conditions.
Public hospitals will become less overburdened, and able to provide better care and
management of disease control.
The Quahog Platform is transformative, as it performs in the following ways:
● Changes and upgrades the way patient analysis is performed.
● Enhances the way doctors make decisions by empowering them with accuracy and
speed.
● Remains watchful of patient health by constantly monitoring to detect deviations.
● Allows for the seamless and rapid flow of data recorded by various other medical
devices.
● Constantly deepens its learning from new patterns.
The Quahog Platform’s ability to output multi-dimensional decisions makes it applicable across
every healthcare avenue, including pharmaceutical research, drug discovery, patient care in
doctor's offices, clinics and hospitals, and self-help home care.
About Quahog Life Sciences
Based on the principles of “Predict, Maintain and Repair,” Quahog Life Sciences' primary
objective is to implement healthcare extension programs. By constantly monitoring body
performance, we can predict risks, and when found, administer maintenance measures and
introduce fall-back repair plans in cases where risks are otherwise unmanageable. Optimizing
bodily performance can be expected to increase the average human lifespan to 150+ years, in
contrast to the current expected average.
Our approach to solving many of the problems of misdiagnosis / countless needless deaths and
the increasing burdens on the healthcare industry may broadly be classified as:

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Prevention:​ Monitoring health data regularly and predicting possible scenarios will help us to
prevent cascading patient effects. The Quahog Platform is designed to record data through the
integration of medical devices (wearables and home kits, etc.) in order to collect periodic data
and continuously provide insights through deep learning, and provide expert recommendations
so as to help patients prevent diseases as well as maintain and optimize their health.
Repair:​ For conditions that have passed the threshold or genetically induced diseases, our
developing solutions include nanomedicine-assisted drug deliveries for genetic repair. We view
human bodily processes as having machine-like automation, and we are confident that cellular
and systemic breakdowns can be returned to healthy, normal functionality with suitable
strategies.
We are looking forward to developing high-precision healthcare devices that act in the best
interest of users to diagnose diseases and optimize remedial care.