Portable device for classification of medical data

• US 20080215514A1
• Filed: 04/10/2008
• Published: 09/04/2008
• Est. Priority Date: 01/07/2005
• Status: Abandoned Application

• Current Assignee: David W. Morgan
• Original Assignee: David W. Morgan
• Inventors: Morgan, David W.
• Application Number:
  US12/082,518
• Publication Number:
  US 20080215514A1
• Time in Patent Office: Days
• US Class Current: 706/20
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A portable device for classification of medical data, the portable device having an artificial neural network and a configuration store having configuration parameter information relating to the artificial neural network, the artificial neural network being configured in accordance with configuration parameter information, the portable device being operable to receive input data, pass the input data to the ANN, and receive an output from the ANN.

Claims

1. A portable device for classification of medical data, the portable device comprising an artificial neural network and a configuration store comprising configuration parameter information relating to the artificial neural network,

the artificial neural network being configured in accordance with configuration parameter information,

the portable device being operable to receive input data,

pass the input data to the artificial neural network, and

receive an output from the artificial neural network.

2. A portable device according to claim 1 wherein the artificial neural network is a probabilistic neural network and more specifically a constructive probabilistic neural network.

3. A portable device according to claim 2 wherein the artificial neural network comprises an input layer, a pattern layer and a summation layer, and wherein the configuration parameter information comprises a first matrix comprising weight information corresponding to the connections between the input layer and the pattern layer, and a second matrix comprising weight information corresponding to the connections between the summation layer and the pattern layer.

4. A portable device according to claim 1 wherein the input data is received from an auxiliary measurement device.

5. A portable device according to claim 4 provided with a wireless connection to the auxiliary measurement device whereby the portable device may receive the input data from the auxiliary measurement device.

6. A portable device according to claim 4 operable to receive an instruction set corresponding to the auxiliary measurement device to be used to generate the input data.

7. A portable device according to claim 1 where the portable device is operable to perform a pre-processing operation on the input data prior to passing the input data to the artificial neural network.

8. A portable device according to claim 1 wherein the portable device is operable to process the information in accordance with an instruction set.

9. A portable device according to claim 1 operable to transmit at least one of the input data and the output from the artificial neural network to an external service.

10. A portable device according to claim 1 operable to receive configuration parameter information from an external service and store the configuration parameter information in the configuration store.

11. A portable device according to claim 10, wherein the external service comprises a training artificial neural network, the training artificial neural network being trained to classify the input data, the configuration parameter information being established from the training artificial neural network.

12. A portable device according to claim 10 operable to receive an instruction set corresponding to the auxiliary measurement device to be used to generate the input data wherein the portable device is operable to receive the information set and the configuration parameter information from the external service.

13. A portable device according to claim 10 which is operable to receive an instruction set corresponding to a further auxiliary measurement device and further configuration parameter information to configure the artificial neural network to classify input data from the further auxiliary medical device.

14. A portable device according to claim 1 wherein the portable device comprises one of a mobile telephone and a personal digital assistant.

15. A method of providing a portable device for classification of medical data, the portable device comprising an artificial neural network and a configuration store for holding configuration parameter information relating to the artificial neural network, the method comprising the steps of,

on a separate device, training an artificial neural net to classify the intended type of input data,

extracting the configuration parameter information from the trained artificial neural net, and

transmitting the configuration parameter information to the portable device.

16. The method of claim 15 further comprising the step of providing an instruction set corresponding to an auxiliary measurement device operable to generate the input data and transmitting the instruction set to the portable device.

17. A method of classifying medical data using a portable device comprising an artificial neural network, comprising the steps of receiving input data, passing the input data to the artificial neural network, and receiving an output from the artificial neural network.

18. A method according to claim 17 comprising the step of transmitting at least one of the output data to an external service.

19. A method according to claim 17 comprising the step of receiving the input data from an auxiliary measurement device.

20. A method according to claim 17 comprising the steps of receiving configuration parameter information relating to the artificial neural network and storing the configuration parameter information in a configuration store of the portable device.

21. A method according to claim 17 wherein the portable device comprises an artificial neural network and a configuration store comprising configuration parameter information relating to the artificial neural network,

the artificial neural network being configured in accordance with configuration parameter information,

the portable device being operable to receive input data,

pass the input data to the artificial neural network, and

receive an output from the artificial neural network.

22. A method according to claim 17 wherein the portable device comprises a configuration store for holding configuration parameter information relating to the artificial neural network, the method comprising the steps of,

on a separate device, training an artificial neural net to classify the intended type of input data,

extracting the configuration parameter information from the trained artificial neural net, and

transmitting the configuration parameter information to the portable device.