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Computer-based neural network system and method for medical diagnosis and interpretation

Patent 5839438 Issued on November 24, 1998. Estimated Expiration Date: Icon_subject September 10, 2016. Estimated Expiration Date is calculated based on simple USPTO term provisions. It does not account for terminal disclaimers, term adjustments, failure to pay maintenance fees, or other factors which might affect the term of a patent.

Patent References

Method for structuring an expert system utilizing one or more neural networks
Patent #: 5455890
Issued on: 10/03/1995
Inventor: Wang

Method and system for differential diagnosis based on clinical and radiological information using artificial neural networks
Patent #: 5463548
Issued on: 10/31/1995
Inventor: Asada, et al.

Apparatus and method for categorizing health care utilization
Patent #: 5486999
Issued on: 01/23/1996
Inventor: Mebane

Method and system for the detection of microcalcifications in digital mammograms Patent #: 5491627
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Inventors

Assignee

Application

No. 712986 filed on 09/10/1996

US Classes:

600/300, DIAGNOSTIC TESTING128/904, TELEPHONE TELEMETRY128/924Using artificial intelligence

Examiners

Primary: Kamm, William E.

Attorney, Agent or Firm

International Class

A61B 019/00

Abstract

A neural network system and method for diagnosing patients' medical conditions provide an efficient aid in identifying and interpreting factors which are significant in the medical diagnosis. The neural network is trained to recognize medical conditions by being provided with input data that is available for a number of patients, and diagnosis made by physicians in each case. Upon completion of the training period the neural network system uses input measurement and interview data to produce a score, or a graded classification, of a patient's medical condition that is accompanied with a diagnosis interpretation. The interpretation is a sorted catalogue of individual factors and interactions that influenced the score. The interpretive facility of the present invention is based on comparison with a set of nominal values for each input factor or interaction. It can assist the physician in making a diagnosis of the patient's condition and can further provide a "second opinion" that may confirm the physician's findings or point to ambiguities that call for a more detailed analysis.

Other References

  • Zurada et al., Sensitivity Analysis for Minimization of Input Data Dimension for Feedforward Neural Network, 1994 IEEE International Symposium on Circuits and Systems, vol. 6, pp. 447-450
  • Srivastava, Rajeshwar Prasad, Automating Judgmental Decisions Using Neural Newworks: A Model for Processing Business Loan Applications; Proceedings of SCS 92: ACM 20th Annual Computer Science Conference, Kansas City, MO, Mar. 3-5, 1992, pp. 351-357
  • Yeung et al., Knowledge Matrix--An Explanation & Knowledge Refinement Facility for a Rule Induced Neural Network, Proceedings of the 12th National Conference on Aritficial Intelligence, vol. 2, pp. 889-894
  • Pau et al., Explanation Facility for Neural Networks; Journal of Intelligent and Robotic Systems 5: 193-206, 1992
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  • Kim et al., A Neural Network Capable of Learning and Inference for Visual Pattern Recognition; Pattern Recognition, vol. 27, No. 10, pp. 1291-1302, 1994
  • Kim et al., A Fuzzy Connectionist Expert System for Visual Pattern Classification; Robotics & Computer-Integrated Manufacturing, vol. 11. No. 3, pp. 233-244, 1994
  • Yoon et al., Integrating Artificial Neural Networks With Rule-Based Expert Systems; Decision Support Systems II (1994) pp. 497-50
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