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

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 re-mediating 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 might 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. Pre-administration 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 might minimize the negative impact of prolonged neglect and support the agile restoration of 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 might also reduce the load on the immune system, thereby increasing the patients’ potential longevity.

Advancing health care delivery systems 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:

A Patient Master or single version of truth (SVOT) for patient data. This greatly facilitates the creation of patient-centric data stacks which include historical patient data collected across multiple devices, test results, prescription regimens, treatment history and the like. Robust patient-centric data enables holistic analysis which is required to increase detection and prediction potential of the second component. An Expert System — A decision engine capable of displaying 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 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 the avoidance of siloed data. The second component is analogous to 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 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 displaying 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 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.

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.

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.

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 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 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.

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

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.

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 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.

Data Collaboration

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 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).

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 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 surgery 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:

Personalized Medicine Machine Learning Recommendations for Preventive Care

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.

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.

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.

— 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 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.

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 possibly helpful, for most people, attaining and/or maintaining optimal health over a lifetime might 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 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 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.

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 trans-formative, 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.