WO2014152963A1 - Modélisation dynamique d'écosystèmes médicaux - Google Patents

Modélisation dynamique d'écosystèmes médicaux Download PDF

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Publication number
WO2014152963A1
WO2014152963A1 PCT/US2014/028419 US2014028419W WO2014152963A1 WO 2014152963 A1 WO2014152963 A1 WO 2014152963A1 US 2014028419 W US2014028419 W US 2014028419W WO 2014152963 A1 WO2014152963 A1 WO 2014152963A1
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Prior art keywords
plot
micro
medical model
individual entity
endpoint
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PCT/US2014/028419
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English (en)
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Camille HODGES
Daniel Hodges
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Hodges Camille
Daniel Hodges
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/067—Enterprise or organisation modelling
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders

Definitions

  • the embodiments described herein relate generally to systems and methods for modeling and, more particularly, to dynamic medical ecosystems modeling.
  • Figure 1 is a block diagram of an example of static moments in patient treatment practice.
  • Figure 2 is a plot of the Life Cycle Line, under an embodiment.
  • Figure 3 is a completed graphical representation of a Life Cycle Line of a first individual, under an embodiment.
  • Figure 4 is a completed graphical representation (post-mortem) of morbid childhood obesity and poor impulse control that has a snowballing effect on the individual's health and wellness, under an embodiment.
  • Figure 5 is a completed graphical representation of a Life Cycle Line where the individual is in the normal zone, and was subject to random environmental factors, under an embodiment.
  • FIG. 6 is a block diagram showing development of the dynamic Medical Ecosystems Model (dMEM), under an embodiment.
  • dMEM dynamic Medical Ecosystems Model
  • Figure 7 shows a flow diagram of Life Cycle Line development, under an embodiment.
  • Figure 8 shows slope changes in the dMEM, under an embodiment.
  • Figure 9 shows the dynamic Life Cycle Line, under an embodiment.
  • Figure 10 shows a plot of a Life Cycle Line depicting an individual who did not incorporate any kind of health monitoring into their lives, an Unmonitored Lifestyle, under an embodiment.
  • Figure 11 shows a plot of a Life Cycle Line depicting a Monitored Lifestyle, under an embodiment.
  • Figure 12 is an example dMEM recording of the first patient, under an embodiment.
  • Figure 13 is an example dMEM recording of the second patient, under an embodiment.
  • Figure 14 shows the dMEM recording of the previous seven-day compressed data compilation of this patient's pain pattern, under an embodiment.
  • Figure 15 is a plot of the cure rate of the patient having only C-spine surgery, under an embodiment.
  • Figure 17 depicts the superimposed similarity between the pain from carpal tunnel syndrome (CTS), and recent onset cervical radiculopathy, under an embodiment.
  • CTS carpal tunnel syndrome
  • Figure 18 illustrates multiple end users linked to the dMEM cloud, under an embodiment.
  • Figure 19 is a block diagram of the dMEM integrated with a supercomputer system, under an embodiment.
  • Figure 20 depicts a helicoid example underlying the dMEM system design, under an embodiment.
  • Figure 21 is a block diagram depicting a dMEMs platform hosting the circadian model, under an embodiment.
  • Figure 22 is a block diagram depicting the dMEMs platform creating a real-time sensing and collecting system running in parallel to human physiology, under an embodiment.
  • Figure 23 shows the dMEMs from the perspective of the application/nano-sensor developer, under an embodiment.
  • Figure 24 shows the dMEMs from the perspective of the public end-user, under an embodiment.
  • Figure 25 shows the dMEMs from the perspective of the active practice physician, under an embodiment.
  • Figure 26 shows an example of the dMEMs from the perspective of the active practice physician when treating a patient following patient discharge, under an embodiment.
  • Figure 27 shows an example of the dMEMs from the perspective of the active practice physician when treating critical care patient, under an embodiment.
  • Figure 28 is a block diagram of the dMEM integrated with medical smart systems, under an embodiment.
  • FIG. 1 is a block diagram of an example of static moments in patient treatment practice. The snapshot of information obtained during the two visits is then used to determine and initiate a medication regime with an antihypertensive agent. For example, a symptomatic 44-year-old overweight male patient complaining of a two- week history of fatigue and dizziness newly presents to a family physician's office at 4 pm on a Friday afternoon.
  • His blood pressure as checked by the office nurse is noted to be marginally elevated and his screening blood profile normal. He is told to follow up in the family physician's office in two weeks. On the second visit the patient's blood pressure remains elevated.
  • This brief momentary dual snapshot of information obtained during two separate visits at different times, and under different conditions, is then used by the physician to access and briefly determine a differential diagnosis as to the probable etiology of the patient's High Blood Pressure. Then the physician will initiate what he or she may deem to be an appropriate medication regime, i.e. with an antihypertensive agent three times per day.
  • the dMEM of an embodiment develops a Life Cycle Line model, or static life cycle, into a dynamic living model as described in detail herein.
  • Figure 2 is a plot of the Life Cycle Line, under an embodiment.
  • a completed (static) Life cycle Line represents the linear graph of an individual's cumulative life, beginning at birth, progressing through normal, subclinical, and clinical zones culminating in death.
  • the slope of an individual's Life Cycle Line may have increased or decreased as a result of life choices.
  • the graphic indicates the male in this case, had a relatively disease free life.
  • Figure 3 is a completed graphical representation of a Life Cycle Line of a first individual, under an embodiment.
  • the individual of this example has properly cared for their health and wellness, living to an age of 90 years with great quality of life, under an embodiment.
  • Even in an individual who is healthy the natural course of events range from normal at birth, with varying transitions into sub-clinical and clinical disease prior to death.
  • the 90-year-old individual maintained a great quality of life, without significant lifetime clinical (symptomatic) disease, as per her Life Cycle Line.
  • Figure 4 is a completed graphical representation (post-mortem) of morbid childhood obesity and poor impulse control that has a snowballing effect on the individual's health and wellness, under an embodiment.
  • the individual crosses into the subclinical zone (elevated blood sugars), eventually crossing into the clinical zone by age 20 with adult onset insulin dependent diabetes.
  • the ability to directly quantify a person's everyday actions into one output is a key concept in the Life Cycle Line.
  • Figure 5 is a completed graphical representation of a Life Cycle Line where the individual is in the normal zone, and was subject to random environmental factors, under an embodiment. His Life Cycle Line went from the normal zone vertically through subclinical and clinical zones into death. He was a healthy twenty-year old who was originally projected to live for at least 83 years. This individual's life was cut drastically short, killed in action in Afghanistan at age 20.
  • Figure 6 is a block diagram showing development of the dMEM, under an embodiment. A hundred years on the sloping lifeline in this model will equal 36,500 days of life, representing a single Life Cycle Line A (continuous uninterrupted life cycle line) of
  • the dMEM of an embodiment includes a 24-hour cycling processor-based (e.g., server, cloud, personal computer, etc.) platform that collects real-time multiples of physiologic data from medical micro sensors (external or internal), while using the cyclic models of C and D, to essentially change the Life Cycle Line from a static to a dynamic entity.
  • the helical recordings of each 24-hour cycle maintain all medical data corresponding to an individual. Daily 24-hour cyclic recordings can then be plotted to a patient's Life Cycle Line.
  • Figure 7 shows a flow diagram of Life Cycle Line development, under an embodiment.
  • the dMEM receives physiological data that includes data of multiple physiological parameters collected in real-time from sensors coupled to an individual subject.
  • the sensors are coupled to or implanted in the subject, and are configured to telemeter the physiological data to the dMEM platform or otherwise offload or download the physiological data to the dMEM platform.
  • the sensors of an embodiment include sensors of any type and/or configuration as appropriate to collection of physiological data from a living entity.
  • the physiological data includes any data or parameters capable of being collected from a human subject.
  • the dMEM Upon receiving the physiological data, the dMEM generates a number of micro plots, where each micro plot represents or corresponds to a particular time period. Each micro plot includes a cyclical plot of the physiological data for a corresponding time period (e.g., 24-hour period, etc.). Thus, each micro plot comprises an integrated plot of all physiological data collected from a subject during the corresponding time period.
  • the dMEM uses the micro plots, the dMEM generates a medical model plot, or Life Cycle Line. In generating the medical model plot, the dMEM plots the micro plots chronologically according to the corresponding time periods, such that a location of an endpoint of each micro plot determines a change in slope of the medical model plot. As described in detail herein, the slope of the medical model plot represents a state of health of the human subject.
  • the successful dMEM from 0 to 90 years equals a connected series of 32,850 points representative of a continuum of days. Furthermore, each point may then be reduced to a non-random reoccurring 24-hour cycle that essentially returns to the same position after completion of a 360 ° rotation over 24 hours. Each hour represents
  • the point in space that ends each monitored cycle determines the change of slope in the dMEM.
  • This slope change correlates to the actions and behaviors of the preceding 24-hour cycle.
  • the dMEM precisely displays changes in an individual's monitored actions and choices for the upcoming day, based upon the choices made the day before.
  • Figure 8 shows slope changes in the dMEM, under an embodiment.
  • Representative drivers for an increased slope include, but are not limited to, the following: aberrant behavioral patterns; excessive alcohol consumption; unhealthy, unbalanced diet; tobacco use; sedentary lifestyle;
  • Representative drivers for a decreased slope include, but are not limited to, the following: no alcohol consumption; healthy balanced caloric diet; no tobacco use; active lifestyle; good mental hygiene; higher education levels; living above poverty level.
  • each 24-hour cycle of monitored physical and metabolic parameters and changes produces a continuous helix representing the true dynamic nature of the dMEM.
  • each previous and succeeding monitored cycle can be directly compared with others. For example, once a baseline of seven consecutive cycles is obtained, these may be sequentially compressed to a single cycle, yielding a weekly compilation. Uniform compression of a month, year, or decade becomes possible as aging data becomes available for compression. The end-user will have multi-sourced feedback available continuously.
  • New medical paradigms are emerging of which the dMEM will be a major component.
  • a first medical paradigm is one in which preemptive "self- healthcare" will virtually eradicate acute and chronic disease states.
  • a second medical paradigm is one that reveals previously undiagnosed disease, significantly augmenting future medical and surgical outcomes.
  • a third medical paradigm is one in which future dynamic medical, biomedical, pharmacological, academic, and epidemiologic research and stratification, changes the face of global health.
  • a fourth medical paradigm is one in which supercomputers, physiology, and medicine become a singular dynamic real-time continuum.
  • Figure 9 shows the dynamic Life Cycle Line, under an embodiment. The Life Cycle Line demonstrates the future point of entry, integration, and flow of the emerging paradigms (I, II, III, IV) in the healthcare continuum. A detailed description of the new medical paradigms follows.
  • FIG. 10 shows a plot of a Life Cycle Line depicting an individual who did not incorporate any kind of health monitoring into their lives, an Unmonitored Lifestyle, under an embodiment. Thus, they did not have feedback regarding how their day-to-day choices truly impacted their future health and longevity. This individual suffered from a massive heart attack at age 40 years due to his aberrant behavioral patterns. He survived the event, but as noted in the Life Cycle Line, from age 40 years until death at age 60 years, the patient remained permanently and totally disabled, dependent upon government resources.
  • Figure 11 shows a plot of a Life Cycle Line depicting a Monitored Lifestyle, under an embodiment.
  • This individual's health was monitored from age 10 years. The data collected continuously from the individual was processed via the dMEM to yield his Life Cycle Line. From the time this individual was a child, he and his parents had the benefit of knowing how his (and his parents') choices were impacting him. The child would learn from a much earlier age which choices in his life are truly healthy. The visual feedback from the Life Cycle Line would provide positive reinforcement for healthful living from a very young age. This preemptive "self-healthcare” would necessarily eliminate 45% of debilitating acute and chronic metabolic disease states in the U.S. Healthcare dollar savings would be tremendous. With the Monitored Lifestyle, the patient was able to see an improved quality of life, improved longevity, and productive lifestyle with absence of disability, until death at 74.
  • Figure 12 is an example dMEM recording of the first patient, under an embodiment.
  • the second patient's pain pattern will demonstrate its true zenith in the mid afternoon, which would indicate degenerative arthritis, as seen in the patient's dMEM recording.
  • Figure 13 is an example dMEM recording of the second patient, under an embodiment. This becomes apparent in the real-time recurrent cycling "movie” while not recognized nor likely considered by modern day “snapshot” medicine. Both patients are misdiagnosed as a result, and neither receives accurate or appropriate care.
  • a 45 -year-old patient presenting with radiating neck, arm, and hand pain represents a second example under the second new medical paradigm.
  • Figure 14 shows the dMEM recording of the previous seven-day compressed data compilation of this patient's pain pattern, under an embodiment.
  • the pain clearly varies during course of the day.
  • the dotted elevation in pain is denoted as primarily sharp neck, shoulder, and forearm pain radiating into the hand.
  • the solid red markers indicate dull pain occurring primarily in the hand and forearm.
  • the pain diagram to an astute clinician using the dMEM, will be obvious and can be easily compared to stored database renderings to confirm the diagnosis, with a probability nearing one.
  • the patient in reality, has pre-existing low-grade carpel tunnel syndrome that is now acute secondary to the double crush from MVA induced acute cervical radiculopathy.
  • the patient has two diagnoses and will need two surgeries: a C- spine surgeiy and carpel tunnel release before he will get total relief. Without the dynamic dMEM compressions he would have likely been diagnosed and treated with C-spine surgery only. His cure rate would have been reduced to 33% with chronic pain and ongoing disability until death.
  • Figure 15 is a plot of the cure rate of the patient having only C-spine surgery, under an embodiment.
  • Figure 16 is a plot of the cure rate of the patient having C-spine and carpel tunnel release surgeries, under an embodiment.
  • Figure 18 illustrates multiple end users linked to the dMEM cloud, under an embodiment.
  • This allows for scalable realtime data acquisition.
  • Multiple end user institutions medical, academic, among others
  • the hypothetical plane may represent an institution's selected area of current study. This could include geographical distribution, age distribution, race distribution, disease prevalence, etc. All of these parameters and more are monitored in real time using the dMEM.
  • Drug companies will use the dMEM to monitor multiple cohorts of study participants in ongoing real-time clinical trials. This will undoubtedly change the dynamic of clinical drug trials with the earliest yet recognition of a drug's efficacy, safety, as well as unanticipated positive or negative collateral side effects.
  • FIG. 19 is a block diagram of the dMEM integrated with a supercomputer system, under an embodiment.
  • the conventional novelty applications running at any one time measuring an individual's vital signs may suffice for the younger health-oriented segment of society.
  • These applications, or apps can be configured to continuously monitor for a "triggering event" such as an irregular heartbeat while an end-user is exercising. At that point where the anomaly is sensed, the data capture on the end user can increase, by initiating other apps (e.g., an app for cardiac enzymes) to monitor associated parameters.
  • a triggering event such as an irregular heartbeat while an end-user is exercising.
  • the data capture on the end user can increase, by initiating other apps (e.g., an app for cardiac enzymes) to monitor associated parameters.
  • the systems of the dMEM of an embodiment are configured to run and monitor 60 to 200 and more integrated real-time apps on an ongoing 24/7 basis.
  • the dMEM makes use of a mammoth data collection- compression architecture with sensitivity extending well beyond linear and planar mappings of 24 hours.
  • the computing hardware, storage and bandwidth for such an endeavor is readily available with cloud-based web-services and data-centers offered by third party providers.
  • the limiting factors will not be computer or hardware capacities, but rather innovative configuration and integration. Medical nano-sensors combined with the dimensionality of real-time human physiology will push present computing architectures into a multi-dimensional framework.
  • Figure 20 depicts a helicoid example underlying the dMEM system design, under an embodiment.
  • FIG. 21 is a block diagram depicting a dMEMs platform hosting the circadian model, under an embodiment.
  • FIG. 22 is a block diagram depicting the dMEMs platform creating a real- time sensing and collecting system running in parallel to human physiology, under an embodiment. Standardization of pre-configured plug and play ports to the cloud platform, the nano-sensor hardware developer need only configure sensor software to interface with the cloud's ports. Each medical nano-sensor developer and their respective software engineers will be provided hands-on tutorials and technical assistance to grasp a thorough understanding of the 3D real-time architecture and the 24/7 operating systems requirements.
  • Figure 23 shows the dMEMs from the perspective of the application/nano-sensor developer, under an embodiment.
  • Figure 24 shows the dMEMs from the perspective of the public end-user, under an embodiment.
  • Approaching the embodiments described herein from the perspective of the public end-user when a new cloud account in opened by an individual, he or she may then, depending upon credentialing be given access to select from approved app/nano- sensor that may be appropriate for public usage.
  • approved app/nano- sensor that may be appropriate for public usage.
  • These will be listed on the cloud-based open public interface, much like an app store.
  • Each app will provide a detailed medically oriented description of available usage for the potential end-user, as well as bundling capabilities, bandwidth needs, ordering instructions for hardware, cloud fees, etc.
  • the site owner may also select to provide viewing rights to other family members, various care providers such as physicians, nurses, home health providers, emergency services providers, hospitals and research institutions, etc.
  • Categories of public self-tracking users include the young health conscious adult who wants daily tracking of basic vital health systems linked and plotted to the Life Cycle Line. Monitored users as a category, may be nursing home patients tethered to family physician, hospital, home health, family members, as well as yet to be created general and specialty monitoring systems.
  • Figure 25 shows the dMEMs from the perspective of the active practice physician, under an embodiment.
  • Approaching the embodiments described herein from the perspective of the active practice physician when a new account is opened in his or her name and credential verification has occurred the physician is given direct access to appropriate (non-public) medical apps commensurate to his/her specialty and training. He or she will be able to potentially link-in to his patient's existing user site and add medically monitored sensors that extend beyond normal public access.
  • Figure 26 shows an example of the dMEMs from the perspective of the active practice physician when treating a patient following patient discharge, under an embodiment.
  • the treating cardiologist in this case, upon patient discharge may wish to continue to follow real-time heart indices post-discharge for two to three weeks.
  • real-time monitoring beyond the hospital stay to the treating physicians handheld or tablet device (perhaps even professional monitoring services), daily medication changes as may become needed would negate what would surely become a hospital re-admission for a similar non- monitored patient.
  • the physician determine what body systems are of most immediate importance to monitor. He will have a handheld tablet with a selection list of medical systems categorized app icons to choose from. The list will have hundreds of individual monitors to choose from as well as lists of single app consolidated nano- sensors. He will make his decision promptly and upon touchpad app selection he will be activating the helical cloud system for immediate recording and feedback. As each nano- sensor or consolidated group of nano-sensors is applied, immediate real-time bedside feedback monitoring is initiated from the Cloud.
  • the physician may select upwards of 50 or more nano-sensors to monitor multi-body systems (e.g., real-time hepatic enzyme flows, cardiac enzymes, renal functions, etc.) all in an effort to preemptively monitor for latent contusional blood loss that could preemptively indicate pending catastrophic organ failure.
  • multi-body systems e.g., real-time hepatic enzyme flows, cardiac enzymes, renal functions, etc.
  • the numbers of acute admission (50) nano-sensor functions being monitored may be gradually pruned as condition allows.
  • Figure 27 shows an example of the dMEMs from the perspective of the active practice physician when treating critical care patient, under an embodiment. The above are just a few cited examples, and in no way are an indication of all potential systems users.
  • FIG. 28 is a block diagram of the dMEM integrated with medical smart systems, under an embodiment.
  • Embodiments described herein include a method comprising receiving physiological data that includes data of a plurality of physiological parameters collected from an individual entity. The method comprises generating a plurality of micro plots.
  • Each micro plot comprises a cyclical plot of the physiological data for a corresponding time period .
  • Each micro plot corresponds to a time period of a plurality of time periods.
  • the method comprises generating a medical model plot comprising the plurality of micro plots.
  • the plurality of micro plots is plotted chronologically according to the plurality of time periods.
  • a location of an endpoint of each micro plot determines a change in slope of the medical model plot.
  • the slope represents a state of health of the individual entity.
  • Embodiments described herein include a method comprising: receiving physiological data that includes data of a plurality of physiological parameters collected from an individual entity; generating a plurality of micro plots, wherein each micro plot comprises a cyclical plot of the physiological data for a corresponding time period, wherein each micro plot corresponds to a time period of a plurality of time periods; and generating a medical model plot comprising the plurality of micro plots, wherein the plurality of micro plots are plotted chronologically according to the plurality of time periods, wherein a location of an endpoint of each micro plot determines a change in slope of the medical model plot, wherein the slope represents a state of health of the individual entity.
  • the physiological data is collected in real-time from sensors coupled to the individual entity.
  • the sensors comprise nano-sensors.
  • the sensors comprise sensors coupled to the individual entity.
  • the sensors comprise sensors implanted in the individual entity.
  • the method comprises continuously collecting the physiological data.
  • the physiological data comprises time data.
  • the physiological data comprises location data.
  • the physiological data comprises physical activity data.
  • the time period of the cyclical plot is a 24-hour period.
  • the cyclical plot is based on a circadian cycle.
  • the micro plot for each time period comprises a start point and the endpoint.
  • each micro plot is located at a same point in a complete rotation that defines the micro plot.
  • the endpoint of each micro plot for each time period is located at a new position in space.
  • the physiological data determines the new position of the endpoint.
  • the endpoint of a micro plot is a start point for a next subsequent micro plot.
  • the medical model plot comprises a continuous helix comprising the plurality of micro plots.
  • the method comprises compressing the data of the plurality of micro plots to form the medical model plot.
  • the method comprises determining the state of health by comparing at least one set of micro plots of the medical model plot.
  • Changes in the slope indicate physical changes in the state of health of the individual entity.
  • the slope of the medical model plot is inversely proportional to a quality of life of the individual entity.
  • the slope of the medical model plot represents longevity of the individual entity.
  • the medical model plot comprises a start point that corresponds to birth of the individual entity.
  • the medical model plot comprises a normal zone, wherein the normal zone represents absence of disease process in the individual entity.
  • the medical model plot comprises a subclinical zone, wherein the subclinical zone represents onset of clinical symptoms in the individual entity.
  • the medical model plot comprises a clinical zone, wherein the clinical zone represents presence of clinical symptoms in the individual entity.
  • the medical model plot comprises an endpoint that corresponds to death of the individual entity.
  • the method comprises providing the medical model plot to the individual entity.
  • the method comprises providing the medical model plot to at least one healthcare provider.
  • the method comprises providing the medical model plot to at least one organization.
  • Embodiments described herein include a system comprising a plurality of sensors coupled to an individual entity.
  • the plurality of sensors collect physiological data that includes data of a plurality of physiological parameters collected from the individual entity.
  • the system includes a platform comprising a processor.
  • the platform is coupled to the plurality of sensors.
  • the processor is running an application, and the application generates a plurality of micro plots.
  • Each micro plot comprises a cyclical plot of the physiological data for a corresponding time period.
  • Each micro plot corresponds to a time period of a plurality of time periods.
  • the application generates a medical model plot comprising the plurality of micro plots.
  • the plurality of micro plots is plotted
  • Embodiments described herein include a system comprising: a plurality of sensors coupled to an individual entity, wherein the plurality of sensors collect physiological data that includes data of a plurality of physiological parameters collected from the individual entity; and a platform comprising a processor, wherein the platform is coupled to the plurality of sensors, wherein the processor is running an application, wherein the application generates a plurality of micro plots, wherein each micro plot comprises a cyclical plot of the physiological data for a corresponding time period, wherein each micro plot corresponds to a time period of a plurality of time periods, wherein the application generates a medical model plot comprising the plurality of micro plots, wherein the plurality of micro plots are plotted chronologically according to the plurality of time periods, wherein a location of an endpoint of each micro plot determines a change in slope of the medical model plot, wherein the slope represents a state of health of the individual entity.
  • the physiological data is collected in real-time from the plurality of sensors.
  • the sensors comprise nano-sensors.
  • the sensors comprise sensors coupled to the individual entity.
  • the system comprises continuously collecting the physiological data.
  • the physiological data comprises location data.
  • the time period of the cyclical plot is a 24-hour period.
  • the cyclical plot is based on a circadian cycle.
  • the micro plot for each time period comprises a start point and the endpoint.
  • the endpoint of each micro plot is located at a same point in a complete rotation that defines the micro plot.
  • the endpoint of each micro plot for each time period is located at a new position in space.
  • the physiological data determines the new position of the endpoint.
  • the endpoint of a micro plot is a start point for a next subsequent micro plot.
  • the medical model plot comprises a continuous helix comprising the plurality of micro plots.
  • the data of the plurality of micro plots is compressed, and the medical model plot comprises the compressed data.
  • the state of health by is determined by comparing at least one set of micro plots of the medical model plot.
  • the change in the slope corresponds to physical changes in the state of health of the individual entity.
  • the slope of the medical model plot is inversely proportional to a quality of life of the individual entity.
  • the slope of the medical model plot corresponds to longevity of the individual entity.
  • the medical model plot comprises a start point that corresponds to birth of the individual entity.
  • the medical model plot comprises a normal zone, wherein the normal zone represents absence of disease process in the individual entity.
  • the medical model plot comprises a subclinical zone, wherein the subclinical zone represents onset of clinical symptoms in the individual entity.
  • the medical model plot comprises a clinical zone, wherein the clinical zone represents presence of clinical symptoms in the individual entity.
  • the medical model plot comprises an endpoint that corresponds to death of the individual entity.
  • the medical model plot is provided to the individual entity.
  • the medical model plot is provided to at least one healthcare provider.
  • the medical model plot is provided to at least one organization.
  • Computer systems and networks suitable for use with the dMEM embodiments described herein include local area networks (LAN), wide area networks (WAN), Internet, or other connection services and network variations such as the world wide web, the public internet, a private internet, a private computer network, a public network, a mobile network, a cellular network, a value-added network, and the like.
  • Computing devices coupled or connected to the network as a component of progressive mechanical intelligence embodiments may be any microprocessor controlled device that permits access to the network, including terminal devices, such as personal computers, workstations, servers, mini computers, main-frame computers, laptop computers, mobile computers, palm top computers, hand held computers, mobile phones, TV set-top boxes, or combinations thereof.
  • the computer network may include one of more LANs, WANs, Internets, and computers.
  • the computers may serve as servers, clients, or a combination thereof.
  • the dMEM can be a component of a single system, multiple systems, and/or geographically separate systems.
  • the dMEM can also be a subcomponent or subsystem of a single system, multiple systems, and/or geographically separate systems.
  • the dMEM can be coupled to one or more other components (not shown) of a host system or a system coupled to the host system.
  • One or more components of the dMEM and/or a corresponding system or application to which the dMEM is coupled or connected includes and/or runs under and/or in association with a processing system.
  • the processing system includes any collection of processor-based devices or computing devices operating together, or components of processing systems or devices, as is known in the art.
  • the processing system can include one or more of a portable computer, portable
  • the portable computer can be any of a number and/or combination of devices selected from among personal computers, personal digital assistants, portable computing devices, and portable communication devices, but is not so limited.
  • the processing system can include components within a larger computer system.
  • the processing system of an embodiment includes at least one processor and at least one memory device or subsystem.
  • the processing system can also include or be coupled to at least one database.
  • the term "processor” as generally used herein refers to any logic processing unit, such as one or more central processing units (CPUs), digital signal processors (DSPs), application-specific integrated circuits (ASIC), etc.
  • the processor and memory can be monolithically integrated onto a single chip, distributed among a number of chips or components, and/or provided by some combination of algorithms.
  • the methods described herein can be implemented in one or more of software algorithm(s), programs, firmware, hardware, components, circuitry, in any combination.
  • Communication paths couple the components and include any medium for communicating or transferring files among the components.
  • the communication paths include wireless connections, wired connections, and hybrid wireless/wired connections.
  • the communication paths also include couplings or connections to networks including local area networks (LANs), metropolitan area networks (MANs), wide area networks (WANs), proprietary networks, interoffice or backend networks, and the Internet.
  • LANs local area networks
  • MANs metropolitan area networks
  • WANs wide area networks
  • proprietary networks interoffice or backend networks
  • the Internet and the Internet.
  • the communication paths include removable fixed mediums like floppy disks, hard disk drives, and CD-ROM disks, as well as flash RAM, Universal Serial Bus (USB) connections, RS-232 connections, telephone lines, buses, and electronic mail messages.
  • USB Universal Serial Bus

Abstract

Selon des modes de réalisation, la présente invention concerne des systèmes et procédés comprenant la réception de données physiologiques comprenant des données d'une pluralité de paramètres physiologiques recueillis en temps réel via des capteurs couplés à une entité individuelle. Des micro-représentations graphiques sont générées, et chaque micro-représentation graphique comporte une représentation graphique cyclique des données physiologiques pour une période de temps correspondante d'une pluralité de périodes de temps. Une représentation graphique de modèle médical est générée pour inclure les micro-représentations graphiques. La formation de la représentation graphique de modèle médical comprend le tracé des micro-représentations graphiques chronologiquement en fonction des périodes de temps. Une localisation d'un point d'extrémité de chaque micro-représentation graphique détermine un changement dans la pente de la représentation graphique de modèle médical, et la pente représente un état de santé de l'entité individuelle.
PCT/US2014/028419 2013-03-14 2014-03-14 Modélisation dynamique d'écosystèmes médicaux WO2014152963A1 (fr)

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US201361783996P 2013-03-14 2013-03-14
US61/783,996 2013-03-14
US201461934090P 2014-01-31 2014-01-31
US61/934,090 2014-01-31
US201461950318P 2014-03-10 2014-03-10
US61/950,318 2014-03-10
US201414205844A 2014-03-12 2014-03-12
US14/205,844 2014-03-12

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US10610624B2 (en) 2013-03-14 2020-04-07 Smith & Nephew, Inc. Reduced pressure therapy blockage detection
US10905806B2 (en) 2013-03-14 2021-02-02 Smith & Nephew, Inc. Reduced pressure wound therapy control and data communication
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US11369730B2 (en) 2016-09-29 2022-06-28 Smith & Nephew, Inc. Construction and protection of components in negative pressure wound therapy systems
US11712508B2 (en) 2017-07-10 2023-08-01 Smith & Nephew, Inc. Systems and methods for directly interacting with communications module of wound therapy apparatus
US11793924B2 (en) 2018-12-19 2023-10-24 T.J.Smith And Nephew, Limited Systems and methods for delivering prescribed wound therapy

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