METHODS AND SYSTEMS FOR PERFORMING A CLINICAL ASSESSMENT

• US 20080234558A1
• Filed: 03/18/2008
• Published: 09/25/2008
• Est. Priority Date: 03/20/2007
• Status: Abandoned Application

• Current Assignee: Cogito Health Inc.
• Original Assignee: Cogito Health Inc.
• Inventors: Jackson, Jonathan, Kumar, Vikram S.
• Application Number:
  US12/050,828
• Publication Number:
  US 20080234558A1
• Time in Patent Office: Days
• US Class Current: 600/306
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention provides method and systems for performing clinical assessment of a patient that includes determining of a base clinical assessment for the patient by generating information on a clinical rating scale. At least one objective signal is recorded, and each objective signal involves an indicator corresponding to the state of the patient or the state of the patient'"'"'s environment. Each objective signal is analyzed for generating a corresponding rating on the clinical rating scale. The clinical assessment of the patient may be provided by combining the information from the base clinical assessment with the information generated from analysis of each objective signal. In an embodiment, the clinical assessment may be based exclusively on information generated by analysis of each objective signal. The methods for performing clinical assessment of a patient may also be provided as computer program products having computer readable instructions embodied therein.

Claims

1. A method for performing clinical assessment of a patient comprising the steps of:

determining a base clinical assessment for the patient comprising generating information based on a clinical rating scale;

recording at least one objective signal, each objective signal comprising an indicator corresponding to the state of said patient or the state of said patient'"'"'s environment;

analyzing each objective signal for generating a corresponding rating on the clinical rating scale;

providing a clinical assessment of said patient on the basis of information generated by analysis of each objective signal.

2. The method as claimed in claim 1, wherein the step of providing the clinical assessment of said patient comprises combining the information generated by the base clinical assessment with the information generated by analysis of each objective signal.

3. The method as claimed in claim 1, wherein the step of providing the clinical assessment of said patient is based exclusively on information generated by analysis of each objective signal.

4. The method as claimed in claim 1, wherein analysis of each objective signal includes relating said signal to the base clinical assessment.

5. The method as claimed in claim 1, wherein analysis of objective signals comprises application of a mathematical model.

6. The method as claimed in claim 5, wherein the mathematical model is improved by the steps of:

determining at least one base clinical assessment and recording a corresponding at least one objective signal for a plurality of patients, wherein each base clinical assessment is obtained at the same time or at nearly the same time as the corresponding objective signal; and

relating each objective signal to a clinical state on the basis of the corresponding base clinical assessment.

7. The method as claimed in claim 5, wherein the mathematical model is improved by the steps of:

determining a plurality of base clinical assessments and recording a plurality of corresponding objective signals for a specific patient, wherein each base clinical assessment is determined at the same time or at nearly the same time as the corresponding objective signal; and

relating each objective signal to a clinical state for the specific patient on the basis of its corresponding base clinical assessment.

8. The method as claimed in claim 5, wherein said mathematical model comprises application of a regression approach.

9. The method as claimed in claim 5, wherein the mathematical model comprises application of neural networks.

10. The method as claimed in claim 1, wherein the clinical rating scale can be classified as falling within one of, scales for social health, scales for psychological well being, scales for anxiety, scales for depression, scales for mental status testing, scales for pain measurements, scales for general health status, and scales for quality of life.

11. The method as claimed in claim 1, wherein the clinical rating scale comprises one of PHQ-9, visual analog scale for pain, APGAR score for neonatal health, Quality of Life scale, or HAM-D.

12. The method as claimed in claims 1, wherein said clinical rating scale is used to assess the state of any of one, psychiatric diseases including depression, bipolar disease, schizophrenia, and anxiety, endocrine diseases including diabetes, cushings syndrome, and thyroid disorders, cardiac conditions including congestive heart disease, hypertension and peripheral vascular disease, pain disorders including chronic pain and back pain, inflammatory diseases including arthritis, inflammatory bowel disease and psoriasis, neurological conditions including epilepsy, headaches and traumatic brain injury, and rehabilitation including post cardiac bypass surgery rehabilitation.

13. The method as claimed in claim 1, wherein the base clinical assessment comprises assessment by a healthcare provider.

14. The method as claimed in claim 1, wherein the base clinical assessment comprises a self-report performed by the patient.

15. The method as claimed in claim 2, wherein objective signals are recorded periodically, to provide updates to the base clinical assessment.

16. The method as claimed in claim 2, wherein the step of combining the information generated by the base clinical assessment with the information generated by analysis of the objective signals comprises application of a mathematical model.

17. The method as claimed in claim 16, wherein said mathematical model comprises a Kalman filter.

18. The method as claimed in claim 1, wherein each objective signal is recorded by a sensor.

19. The method as claimed in claim 1, wherein the objective signal comprises the galvanic skin conductance recorded from the patient.

20. The method as claimed in claim 1, wherein the objective signal comprises a recorded speech sample from the patient.

21. The method as claimed in claim 20, wherein based on the clinical rating generated for an objective signal, the patient is subjected to an additional clinical assessment on the clinical rating scale.

22. The method as claimed in claim 21, wherein the recorded speech sample is provided over a communication device, including a phone.

23. The method as claimed in claim 1, wherein the base clinical assessment is obtained from the patient over a communication device, including a phone.

24. The method as claimed in claim 23, wherein the base clinical assessment is recorded by an Interactive Voice Response (IVR) Server.

25. The method as claimed in claim 24, wherein the objective signal comprises a speech sample recorded by an IVR.

26. The method as claimed in claim 20, wherein analyzing the objective signal comprises applying speech analysis techniques to extract voice features.

27. The method as claimed in claim 26, wherein extraction of voice features comprises the steps of:

identification of voiced segments of a speech sample; and

extraction of voice features from voiced segments of said speech sample.

28. The method as claimed in claim 27, wherein identification of voiced segments of said speech sample comprises applying a two-level Hidden Markov Model.

29. The method as claimed in claim 28, wherein the two-level Hidden Markov Model uses at least one of autocorrelation, entropy, and residual amplitude structure of speech samples.

30. The method as claimed in claim 29, wherein the two-level Hidden Markov Model uses 30 millisecond speech samples.

31. The method as claimed in claim 30, wherein identification of voiced segments is iteratively improved using the Baum-Welch Expectation Maximization technique.

32. The method as claimed in claim 23, wherein the extracted voice features comprise Class I features and Class II features.

33. The method as claimed in claim 32, wherein said Class I features comprise at least one of formant frequency, confidence in formant frequency, spectral entropy, value of largest autocorrelation peak, location of largest autocorrelation peak, number of autocorrelation peaks, energy in frame and time derivative of energy in frame.

34. The method as claimed in claim 32, wherein said Class II features comprise at least one of average length of voiced segment, average length of speaking segment, fraction of time speaking, voicing rate, fraction speaking over, average number of short speaking segments per minute, entropy of speaking lengths and entropy of pause lengths.

35. The method as claimed in claim 26, wherein the step of analyzing the objective signal and correlating it to the clinical rating scale comprises providing inputs from a plurality of models (m) and uniquely corresponding meta models (m′

) to a neural network for generating information correlating said objective signal to the clinical rating scale, wherein said models (m) and meta models (m′

) provide said inputs on the basis of voice features extracted from the objective signal.

36. The method as claimed in claim 35, wherein each model (m) predicts a score on the clinical rating scale.

37. The method as claimed in claim 36, wherein each meta model (m′

) provides a confidence rating to the neural network.

38. The method as claimed in claim 37, wherein said confidence rating comprises a higher rating when the respective model (m) is probabilistically correct, and a lower rating when the respective model (m) is probabilistically incorrect.

39. A computer program product for performing clinical assessment of a patient comprising a computer readable medium having computer readable program code for:

obtaining a base clinical assessment for the patient comprising information based on a clinical rating scale;

recording at least one objective signal, each objective signal comprising an indicator corresponding to the state of said patient or the state of said patient'"'"'s environment;

analyzing each objective signal for generating a corresponding rating on the clinical rating scale, said analysis including reference to the base clinical assessment;

providing a clinical assessment of said patient on the basis of information generated by analysis of each objective signal.

40-76. -76. (canceled)