Automatic etiology sequencing system and method

• US 8,740,789 B2
• Filed: 03/03/2005
• Issued: 06/03/2014
• Est. Priority Date: 03/03/2005
• Status: Expired due to Fees

• Current Assignee: Cardiac Pacemakers Incorporated (Boston Scientific Corporation)
• Original Assignee: Cardiac Pacemakers Incorporated (Boston Scientific Corporation)
• Inventors: Brockway, Marina, Johnson, David, Goscha, Don, Kadhiresan, Veerichetty A., Srivathsa, Muralidharan, Haeder, Lisa
• Primary Examiner(s): Thomson, Bill
• Assistant Examiner(s): Archer, Marie
• Application Number:
  US11/071,646
• Publication Number:
  US 20060200007A1
• Time in Patent Office: 3,379 Days
• Field of Search: 600300-301, 705 2- 4, 128903-905, 128920-925, 3405731-576, 3791061-1062, 706 45- 47, 706/21, 706/924, 706 15- 16, 702/19
• US Class Current: 600/300
• CPC Class Codes: A61B 5/00 Measuring for diagnostic purposes radiation diagnosis A61B6/00; diagnosis by ultrasonic, sonic or infrasonic waves A61B8/00; Identification of persons
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/686 Permanently implanted devices, e.g. pacemakers, other stimulators, biochips A61B5/6861 takes precedence
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: G06N 3/02 Neural networks
• CPC Class Codes: G06N 3/042 Knowledge-based neural networks; Logical representations of neural networks
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 70/60 relating to pathologies
• CPC Class Codes: Y10S 128/92 Computer assisted medical diagnostics
• CPC Class Codes: Y10S 128/924 using artificial intelligence
• CPC Class Codes: Y10S 128/925 Neural network
• CPC Class Codes: Y10S 706/924 : Medical

Abstract

A system and related method for identifying a trigger event to a patient health-related exacerbation. The system includes at least one sensor configured to collect data related to the patient health-related exacerbation, an analyzer configured to validate the collected data, a patient interface device configured to receive patient inputs to at least one question, and an identifier device configured to receive the patient inputs and collected data and identify the trigger event. The system may also include an expert system configured to receive the patient inputs and the validated collected data and identify a primary patient disease, and a reporting device configured to generate reports related to the identified primary patient disease, patient inputs, and sensed patient conditions.

Claims

1. A method for determining a primary source of exacerbation of a health of a patient having a chronic illness, the method comprising the steps of:

collecting sensor data indicative of at least one patient physical condition;

cross-correlating the collected sensor data to validate the collected data;

collecting patient responses to an interactive questionnaire;

identifying a trigger event for a patient health exacerbation based on the collected data and patient responses,

wherein the trigger event is determined by a neural-network-based learning module in a computer;

identifying a primary disease associated with the trigger event using an expert system;

and

reporting the identified primary disease and the primary source of the exacerbation to a caregiver.

2. The method of claim 1, further comprising validating the identified primary disease and trigger event by a caregiver.

3. The method of claim 2, further comprising updating the expert system with information related to the validated primary disease and trigger event.

4. The method of claim 1, further comprising sequencing the collected data.

5. The method of claim 1, wherein cross-correlation of the collected data includes comparing the collected data with data collected from another patient sensor.

6. The method of claim 1, wherein cross-correlation of the collected data includes comparing the collected data with stored patient data.

7. The method of claim 6, wherein the stored patient data includes information selected from the group consisting of the patient'"'"'s family health history, the patient'"'"'s previous health history, a pharmaceutical database that contains information related to pharmacodynamics, drug side effects, drug compatibility, and pharmacogenomics, clinical trials, environmental conditions, physician guidelines, and population trends.

8. The method of claim 1. further comprising generating an abnormality signal if cross-correlation of the collected sensor data indicates an abnormality.

9. The method of claim 8, further comprising responding to the abnormality signal with a report provided to the patient or to a caregiver.

10. The method of claim 1, wherein the interactive questionnaire includes questions related to an adaptable scenario.

11. The method of claim 1, further comprising verifying the primary disease.

12. The method of claim 11, wherein the cross-correlation is performed with an analyzer, and the method further includes reporting verification of the primary disease to the analyzer to aid in system learning.

13. An automated system configured to determine a primary source of patient health-related exacerbation, the system comprising:

at least one sensor to collect data related to the patient health-related exacerbation;

an analyzer to validate the collected data;

a patient interface device to receive patient inputs to at least one question;

an identifier device to receive the patient inputs and collected data and to identify a trigger event for a patient health-related exacerbation,

wherein the identifier device includes a neural network-based learning module; and

an expert system configured to receive the patient inputs and the collected data and identify a primary patient disease.

14. The system of claim 13, further comprising a reporting device configured to generate reports related to the identified primary patient disease, patient inputs, and sensed patient conditions.

15. The system of claim 14, wherein the reports are used by the identifier device to determine the primary source.

16. The system of claim 13, further comprising a sequencer configured to sequence the collected data.

17. The system of claim 16, wherein the sequencer initiates sequencing in response to a predetermined patient input or a cross-correlation result from the analyzer,

18. The system of claim 13, wherein the patient interface device includes at least one of a keypad, touch control screen or other input device.

19. The system of claim 18, wherein the patient interface device is configured to present a series of questions in the form of a questionnaire, the questionnaire being structured as an adaptable scenario.

20. The system of claim 13, wherein the analyzer is configured to receive verification of the identified primary disease to aid in a learning function of the analyzer.

21. The system of claim 13, wherein the analyzer cross-correlates the sensed data with data from another patient sensor.

22. The system of claim 13, wherein the analyzer cross-correlates the sensed data with stored patient data sensed previously.

23. The system of claim 13, wherein the analyzer cross-correlates the sensed data with data related to the patient'"'"'s family health history, a pharmaceutical database that contains information on pharmacodynamics, drug side effects, drug compatibility, and pharmacogenotnics, clinical trials, environmental conditions, and guidelines.

24. The system of claim 13, wherein the analyzer is configured to detect abnormalities in the at least one sensor resulting from the cross-correlation.

25. A method of automatically determining a primary patient disease that causes a patient health-related exacerbation, the method comprising the steps of:

collecting sensor data indicative of at least one patient physical condition;

cross-correlating the collected sensor data to validate the collected data;

collecting patient responses to an interactive questionnaire; and

determining the primary patient disease using an expert computer system, the validated collected data, and the patient responses, wherein the primary patient disease is determined by a neural network-based learning module in the computer system and wherein determining the primary patient disease includes determining two or more disease subclasses.

26. The method of claim 25, further comprising the step of determining a primary source of health exacerbation based on the collected data, the patient responses, and the determined patient disease.