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1. WO2019122919 - A MEDICAL INTERVENTION CONTROL SYSTEM

Note: Text based on automatic Optical Character Recognition processes. Please use the PDF version for legal matters

[ EN ]

CLAIMS:

1. A medical intervention control system for providing a risk analysis and influencing intervention action on a patient, the system comprising:

a database with a data set containing data from at least one data source comprising: a) study data; and b) sensed data;

a waveform detector operable to identify a waveform from a data source, extract the waveform, categorise the waveform, normalise the waveform to a predetermined format and determine waveform characteristics and parameters of the waveform, the waveform detector populating part of the sensed data;

a measurement module to derive subject data from the patient;

an analyser operable to analyse the subject data with respect to the data set from the at least one data source and output an associated probability for each of one or more outcomes, wherein the associated probability is affected by an intervention, wherein the analyser takes subject data derived from the patient and tests for outcomes and potential interventions which influence the outcomes;

an action and alert management module to provide feedback to an intervention allocation module and, for respective interventions, being operable to output a direct instruction to an intervention allocation module to perform an intervention or a direct instruction to an intervention allocation module to desist from performing an intervention; and

an intervention allocation module to perform an intervention or desist from an intervention depending on the direct instruction from the action and alert management module on the current patient.

2. The system according to claim 1 , wherein the waveform is identified from a data source comprising an image.

3. A medical evaluation and prediction system for providing a risk analysis and influencing intervention action on a patient, the system comprising:

a database with a data set containing data from at least one data source;

a measurement module to derive subject data from the patient;

an analyser operable to analyse the subject data with respect to the data set from the at least one data source and output an associated probability for each of one or more outcomes, wherein the associated probability is affected by an intervention.

4. The system of any preceding claim, wherein the analyser builds up a probability matrix trained to deliver a risk analysis of various“treat” /“no treat” options based on trained data and the system delivers a risk analysis of what the risk is if the patient is treated with an option or the risk if the patient is not treated with a treatment option.

5. The system of any preceding claim, wherein the output of the action and alert management module includes a set of instructions relating to a respective intervention for the intervention allocation module.

6. The system of any preceding claim, wherein the intervention allocation module includes a medical robot operable to perform the intervention and at least one step of the intervention to be undertaken by the robot requires an authorisation input from a human user.

7. The system of any preceding claim, wherein subject data is sensed subsequent to an intervention to re-evaluate the patient’s condition.

8. The system of any preceding claim, wherein the study data comprises: historic data which includes patient risk factors, patient demographics, associated clinical outcomes and results, historic sensed data and medical

insurance profiles or social media feeds, Registry data; Actuarial risk tables; Clinical trial data; and/or Audit data.

9. The system of any preceding claim, wherein the sensed data comprises: captured or sensed data points, waveforms or images obtained from sensing equipment such as electrocardiograms, coronary pressure wires and transducers, angiograms, ultrasonic transducers, coronary guidewire-mounted sensors to provide data on Fractional Flow Reserve - FFR, iFR (instant wave-free ratio (iFR) version of FFR), coronary flow reserve - CFR, the relationship between resting distal coronary pressure to aortic pressure ratio (Pd/Pa), medical insurance profiles and/or social media feeds.

10. The system of any preceding claim, wherein the data comprises inputs for predictive models in the analyser.

11. The system of any preceding claim, wherein the system further comprises a normaliser to normalise the data in the data set.

12. The system of any preceding claim, wherein the normaliser fits the data into a standard template.

13. The system of any preceding claim, wherein the analyser comprises a plurality of analysers, one or more of which analyses the data from a data source and attaches a probability model of likely outcomes for respective interventions.

14. The system according to any preceding claim, wherein a risk analysis assessment module is operable to provide a risk analysis based on the output of the analyser.

15. The system of any preceding claim, wherein the waveform detector is operable to transform image data of a waveform to provide a standard output which can then be used to train the analyser.

16. The system of any preceding claim, wherein probability node values from each input are run in a matrix, where each value is iterated by further input from the fields to determine a series of probability node values for each of the inputs when used individually or when used in consort with other inputs and the series of node values determine the need for treatment, the probability of significant mortality and morbidity, risk analysis and the ability to

discriminate between focal and diffuse disease and a probability is assigned to each outcome or potential intervention so as to then make a determination, potentially in consultation with a health professional or to better inform the practicing health professional about what outcomes or interventions to consider.

17. A method for providing a risk analysis and influencing intervention action on a patient, the method comprising:

establishing a database with a data set containing data from at least one data source comprising: a) study data; and b) sensed data;

populating part of the sensed data with waveform data, wherein the waveform is extracted from a data source and normalised to a predetermined format;

deriving subject data from the patient;

testing the subject data for outcomes and potential interventions which influence the outcomes for the patient;

analysing the subject data with respect to the data set from the at least one data source and outputting an associated probability for respective outcomes, wherein the associated probability is affected by an intervention; outputting, for respective interventions, a direct instruction to perform an intervention or a direct instruction to desist from performing an intervention; and

performing the intervention or desisting from the intervention depending on the direct instruction.

18. A computer operable medium programmed with a set of instructions to: identify a waveform from a data source;

determine waveform characteristics and parameters of the waveform, derive subject data from the patient;

analyse the subject data with respect to data from at least one data source comprising: a) study data; and b) sensed data;

output an associated probability for each of one or more outcomes, wherein the associated probability is affected by an intervention;

test the subject data for outcomes and interventions;

provide feedback and influence intervention or lack of intervention on the patient.

19. A computer operable medium programmed with a set of instructions to carry out the method of claim 17.