초록 ▼

Methods are provided for developing medical diagnostic tests using decision-support systems, such as neural networks. Patient data or information, typically patient history or clinical data, are analyzed by the decision-support systems to identify important or relevant variables and decision-support

Methods are provided for developing medical diagnostic tests using decision-support systems, such as neural networks. Patient data or information, typically patient history or clinical data, are analyzed by the decision-support systems to identify important or relevant variables and decision-support systems are trained on the patient data. Patient data are augmented by biochemical test data, or results, where available, to refine performance. The resulting decision-support systems are employed to evaluate specific observation values and test results, to guide the development of biochemical or other diagnostic tests, too assess a course of treatment, to identify new diagnostic tests and disease markers, to identify useful therapies, and to provide the decision-support functionality for the test. Methods for identification of important input variables for a medical diagnostic tests for use in training the decision-support systems to guide the development of the tests, for improving the sensitivity and specificity of such tests, and for selecting diagnostic tests that improve overall diagnosis of, or potential for, a disease state and that permit the effectiveness of a selected therapeutic protocol to be assessed are provided. The methods for identification can be applied in any field in which statistics are used to determine outcomes. A method for evaluating the effectiveness of any given diagnostic test is also provided.

대표청구항 ▼

Methods are provided for developing medical diagnostic tests using decision-support systems, such as neural networks. Patient data or information, typically patient history or clinical data, are analyzed by the decision-support systems to identify important or relevant variables and decision-support

Methods are provided for developing medical diagnostic tests using decision-support systems, such as neural networks. Patient data or information, typically patient history or clinical data, are analyzed by the decision-support systems to identify important or relevant variables and decision-support systems are trained on the patient data. Patient data are augmented by biochemical test data, or results, where available, to refine performance. The resulting decision-support systems are employed to evaluate specific observation values and test results, to guide the development of biochemical or other diagnostic tests, too assess a course of treatment, to identify new diagnostic tests and disease markers, to identify useful therapies, and to provide the decision-support functionality for the test. Methods for identification of important input variables for a medical diagnostic tests for use in training the decision-support systems to guide the development of the tests, for improving the sensitivity and specificity of such tests, and for selecting diagnostic tests that improve overall diagnosis of, or potential for, a disease state and that permit the effectiveness of a selected therapeutic protocol to be assessed are provided. The methods for identification can be applied in any field in which statistics are used to determine outcomes. A method for evaluating the effectiveness of any given diagnostic test is also provided. ) which does not solve a problem and activates another execution component (2,4,5). In this way, the computer system itself selects and organizes execution components (2,4,5) in adjustments (3,6) so that problems can be solved efficiently by the execution components (2,4,5). In particular, the computer system can be applied in fields that require an automatic adaption of its programs and its knowledge to hardly predictable new problems and situations. Examples of such fields of application are image and language processing and robotics. l envelope by a predetermined set of frequency domain window functions, wherein each window occupies a narrow range of frequencies, and computing the integral thereof, and an assignment unit coupled to the integrator for deriving a set of predetermined functions of said integrals and assigning to respective components of a corresponding feature vector in said series of feature vectors; a pitch detector coupled to the feature extraction module for computing respective pitch values of the speech signal at said successive instances of time, a features compression module having a quantization scheme for the feature vectors for compressing the feature vectors, a pitch compression module having a quantization scheme for the pitch values for compressing the pitch values, and a multiplexer for combining the compressed feature vectors and pitch values into a bit-stream. 4. The encoder according to claim 3, further including: at least one auxiliary encoder for encoding auxiliary data other than the feature vectors and pitch values, and creating an enhanced bit-stream including the encoded auxiliary data. 5. The encoder according to claim 4, wherein: the feature vectors contain basic Mel-frequency Cepstral coefficients used for speech recognition, and the auxiliary data are auxiliary Mel-frequency Cepstral coefficients added to enhance the decoded speech quality. 6. The encoder according to claim 3, wherein the feature vectors contain Mel-frequency Cepstral coefficients (MFCC). 7. A method for decoding a bit-stream representing a compressed series of acoustic vectors each containing a respective feature vector and a respective pitch value derived at a respective instance of time, each of the feature vectors having multiple components obtained by: i) deriving at successive instances of time an estimate of the spectral envelope of a digitized speech signal, ii) multiplying each estimate of the spectral envelope by a predetermined set of frequency domain window functions, wherein each window occupies a narrow range of frequencies, and computing the integral thereof, and iii) assigning said integrals or a set of predetermined functions thereof to a respective one of said components of the feature vector; said method comprising the steps of: (a) separating the received bit-stream into compressed feature vectors data and compressed pitch values data, (b) decompressing the compressed feature vectors data and outputting quantized feature vectors, (c) decompressing the compressed pitch values data and outputting quantized pitch values, and (d) generating a continuous speech signal, using the quantized feature vectors and pitch values. 8. The method according to claim 7, wherein the bit-stream is derived from a filtered input speech signal, and there is further included the step of: inverting the effect of the filtering, to produce a reconstruction of the input speech signal prior to filtering. 9. A method for decoding a received bit-stream representing a compressed series of acoustic vectors each containing a respective feature vector, a respective pitch value and respective auxiliary data all derived at a respective instance of time, each of the feature vectors having multiple components obtained by: i) deriving at successive instances of time an estimate of a spectral envelope of a digitized speech signal, ii) multiplying each estimate of the spectral envelope by a predetermined set of frequency domain window functions, wherein each window occupies a narrow range of frequencies, and computing the integral thereof, and iii) assigning said integrals or a set of predetermined functions thereof to a respective one of said components of the feature vector; said method comprising the steps of: (a) separating the received bit-stream into compressed feature vectors data, compressed auxiliary data and compressed pitch values data, (b) decompressing the compressed feature vectors data and outputting quan tized feature vectors, (c) decompressing the compressed pitch values data and outputting quantized pitch values, (d) decompressing the compressed auxiliary data and outputting quantized auxiliary data, and (e) generating a continuous speech signal, using the quantized feature vectors, pitch values and auxiliary data. 10. A decoder for a speech coding system, said decoder being responsive to a received bit-stream representing a series of compressed acoustic vectors each containing a respective feature vector and a respective pitch value derived at a respective instance of time, each of the feature vectors having multiple components obtained by: i) deriving at successive instances of time an estimate of a spectral envelope of a digitized speech signal, ii) multiplying each estimate of the spectral envelope by a predetermined set of frequency domain window functions, wherein each window occupies a narrow range of frequencies, and computing the integral thereof, and iii) assigning said integral or a set of predetermined functions thereof to a respective one of said components of the feature vector; said decoder comprising: a demultiplexer for separating the received bit-stream into compressed feature vectors data and compressed pitch values data, a features decompression module coupled to the demultiplexer for decompressing the compressed feature vectors data and outputting quantized feature vectors, a pitch decompression module coupled to the de-multiplexer for decompressing the compressed pitch values data and outputting quantized pitch values, and a reconstruction module coupled to the features decompression module and to the pitch decompression module for generating a continuous speech signal, using the quantized feature vectors and pitch values. 11. The decoder according to claim 10, wherein: the received bit-stream is derived from a filtered input speech signal, and the reconstruction module includes a filter cancellation unit for inverting the effect of the filtering, to produce a reconstruction of the input speech signal prior to filtering. 12. A decoder for a speech coding system, said decoder being responsive to a received bit-stream representing a series of compressed acoustic vectors each containing a respective feature vector, a respective pitch value and respective auxiliary data all derived at a respective instance of time, each of the feature vectors having multiple components obtained by: i) deriving at successive instances of time an estimate of a spectral envelope of a digitized speech signal, ii) multiplying each estimate of the spectral envelope by a predetermined set of frequency domain window functions, wherein each window occupies a narrow range of frequencies, and computing the integral thereof, and iii) assigning said integral or a set of predetermined functions thereof to a respective one of said components of the feature vector; said decoder comprising: a demultiplexer for separating the received bitstream into compressed feature vectors data and compressed pitch values data, a features decompression module coupled to the demultiplexer for decompressing the compressed feature vectors data and outputting quantized feature vectors, a pitch decompression module coupled to the demultiplexer for decompressing the compressed pitch values data and outputting quantized pitch values, an auxiliary data decompression module coupled to the demultiplexer for decompressing the compressed auxiliary data and outputting quantized auxiliary data, and a reconstruction module coupled to the features decompression module, to the pitch decompression module and to the auxiliary data decompression module for generating a continuous speech signal, using the quantized feature vectors, pitch values and auxiliary data. 13. The decoder according to claim 12, wherein: the feature vectors contain basic Mel-frequency Cepstral coefficients used for speech recognition, and the auxiliary da ta includes auxiliary Mel-frequency Cepstral coefficients added to enhance the decoded speech quality. 14. A dual purpose speech recognition/playback system for voice recognition and reproduction of an encoded speech signal, said dual purpose speech recognition/-playback system comprising a decoder and a recognition unit: the decoder comprising means for decoding and playing back a bit-stream representing a series of compressed acoustic vectors each containing a respective feature vector obtained by deriving at successive instances of time an estimate of a spectral envelope of the digitized speech signal, for multiplying each estimate of the spectral envelope by a predetermined set of frequency domain window functions, wherein each window is non-zero over a narrow range of frequencies, and for computing the integrals thereof, and said series of compressed acoustic vectors further containing a respective pitch value derived at a respective instance of time, said decoder including: a demultiplexer for separating the bit-stream into compressed feature vectors and compressed pitch values, a features decompression module coupled to the demultiplexer for decompressing the feature vectors data and outputting quantized feature vectors, a pitch decompression module coupled to the demultiplexer for decompressing pitch values data embedded in the encoded speech signal and outputting quantized pitch values, and a reconstruction module coupled to the features decompression module and to the pitch decompression module for generating a continuous speech signal, using the quantized feature vectors and pitch values; and the recognition unit being responsive to the decoded feature vectors for continuous speech recognition. 15. The dual purpose speech recognition/playback system according to claim 14, wherein the recognition unit is further responsive to the decoded pitch values for continuous speech recognition. 16. The dual purpose speech recognition/playback system according to claim 14, wherein: the decoder further includes an auxiliary data decompression module coupled to the demultiplexer for decompressing compressed auxiliary data embedded within the bit stream in addition to the feature vectors data and pitch values data for generating quantized auxiliary data, and the reconstruction module is further coupled to the auxiliary data decompression module for generating said continuous speech signal using the quantized auxiliary data. 17. A dual purpose encoder and voice recognition system for encoding a speech signal so as to generate data capable of being decoded as speech and continuous voice recognition, the encoder comprising: a feature extraction module for computing a series of feature vectors from the input speech signal at successive instances of time, the feature extraction module including: a spectrum estimator for deriving an estimate of a series of spectral envelopes at successive instances of time of the digitized speech signal, an integrator coupled to the spectrum estimator for multiplying the spectral envelope by a predetermined set of frequency domain window functions, wherein each window occupies a narrow range of frequencies, and computing the integral thereof, and an assignment unit coupled to the integrator for deriving a set of predetermined functions of said integrals and assigning to respective components of a corresponding feature vector in said series of feature vectors; a pitch detector coupled to the feature extraction module for computing respective pitch values of the speech signal at a given set of instances, and a recognition unit coupled to the feature extraction module and being responsive to the feature vectors for continuous speech recognition. 18. The recognition-adapted encoder according to claim 17, wherein: the recognition system uses the feature vectors computed by the encoder in addition to the computed pitch values for continuous speech recognition. 19. A computer p rogram product comprising a computer useable medium having computer readable program code embodied therein for encoding a digitized speech signal so as to generate data capable of being decoded as speech, the computer readable program code comprising: computer readable program code for causing the computer to convert the digitized speech signal to a series of feature vectors by: i) deriving at successive instances of time an estimate of the spectral envelope of the digitized speech signal, ii) multiplying each estimate of the spectral envelope by a predetermined set of frequency domain window functions, wherein each window is non-zero over a narrow range of frequencies, and computing the integrals thereof, and iii) assigning said integrals or a set of predetermined functions thereof to respective components of a corresponding feature vector in said series of feature vectors; computer readable program code for causing the computer to compute for each instance of time a respective pitch value of the digitized speech signal, and computer readable program code for causing the computer to compress successive acoustic vectors each containing the respective pitch value and feature vector so as to derive therefrom a bit stream. 20. A computer program product comprising a computer useable medium having computer readable program code embodied therein for encoding a digitized speech signal so as to generate data capable of being decoded as speech, the computer readable program code comprising: computer readable program code for causing the computer to convert the digitized speech signal to a series of feature vectors by: i) deriving at successive instances of time an estimate of the spectral envelope of the digitized speech signal, ii) multiplying each estimate of the spectral envelope by a predetermined set of frequency domain window functions, wherein each window occupies a narrow range of frequencies, and computing the integrals thereof, and iii) assigning said integrals or a set of predetermined functions thereof to respective components of a corresponding feature vector in said series of feature vectors; computer readable program code for causing the computer to compute for each instance of time a respective pitch value of the digitized speech signal, computer readable program code for causing the computer to obtain for each instance of time respective auxiliary data other than the feature vectors and pitch values, computer readable program code for causing the computer to compress the feature vectors and the respective pitch values, and computer readable program code for causing the computer to compress successive acoustic vectors each containing the respective pitch value, auxiliary data and feature vector so as to derive therefrom a bit stream. 21. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for encoding a digitized speech signal so as to generate data capable of being decoded as speech, the method steps comprising: (a) converting the digitized speech signal to a series of feature vectors by: i) deriving at successive instances of time an estimate of the spectral envelope of the digitized speech signal, ii) multiplying each estimate of the spectral envelope by a predetermined set of frequency domain window functions, wherein each window is non-zero over a narrow range of frequencies, and computing the integrals thereof, and iii) assigning said integrals or a set of predetermined functions thereof to respective components of a corresponding feature vector in said series of feature vectors; (b) computing for each instance of time a respective pitch value of the digitized speech signal, and (c) compressing successive acoustic vectors each containing the respective pitch value and feature vector so as to derive therefrom a bit stream. 22. A program storage device readable by machine, tangibly embody ing a program of instructions executable by the machine to perform method steps for encoding a digitized speech signal so as to generate data capable of being decoded as speech, the method steps comprising: (a) converting the digitized speech signal to a series of feature vectors by: i) deriving at successive instances of time an estimate of the spectral envelope of the digitized speech signal, ii) multiplying each estimate of the spectral envelope by a predetermined set of frequency domain window functions, wherein each window occupies a narrow range of frequencies, and computing the integrals thereof, and iii) assigning said integrals or a set of predetermined functions thereof to respective components of a corresponding feature vector in said series of feature vectors; (b) computing for each instance of time a respective pitch value of the digitized speech signal, (c) obtaining for each instance of time respective auxiliary data other than the feature vectors and pitch values, (d) compressing the feature vectors and the respective pitch values, and (e) compressing successive acoustic vectors each containing the respective pitch value, auxiliary data and feature vector so as to derive therefrom a bit stream. 23. A computer program product comprising a computer useable medium having computer readable program code embodied therein for decoding a bit-stream representing a compressed series of acoustic vectors each containing a respective feature vector and a respective pitch value derived at a respective instance of time, each of the feature vectors having multiple components obtained by: i) deriving at successive instances of time an estimate of the spectral envelope of a digitized speech signal, ii) multiplying each estimate of the spectral envelope by a predetermined set of frequency domain window functions, wherein each window occupies a narrow range of frequencies, and computing the integral thereof, and iii) assigning said integrals or a set of predetermined functions thereof to a respective one of said components of the feature vector; said computer program product comprising: computer readable program code for causing the computer to separate the received bit-stream into compressed feature vectors data and compressed pitch values data, computer readable program code for causing the computer to decompress the compressed feature vectors data and outputting quantized feature vectors, computer readable program code for causing the computer to decompress the compressed pitch values data and outputting quantized pitch values, and computer readable program code for causing the computer to generate a continuous speech signal, using the quantized feature vectors and pitch values. 24. A computer program product comprising a computer useable medium having computer readable program code embodied therein for decoding a bit-stream representing a compressed series of acoustic vectors each containing a respective feature vector, a respective pitch value and respective auxiliary data all derived at a respective instance of time, each of the feature vectors having multiple components obtained by: i) deriving at successive instances of time an estimate of the spectral envelope of a digitized speech signal, ii) multiplying each estimate of the spectral envelope by a predetermined set of frequency domain window functions, wherein each window occupies a narrow range of frequencies, and computing the integral thereof, and iii) assigning said integrals or a set of predetermined functions thereof to a respective one of said components of the feature vector; said computer program product comprising: computer readable program code for causing the computer to separate the received bit-stream into compressed feature vectors data, compressed auxiliary data and compressed pitch values data, computer readable program code for causing the computer to decompress the compressed feature vectors data and outputting quantized feature vectors, computer readable program code for causing the computer to decompress the compressed pitch values data and outputting quantized pitch values, computer readable program code for causing the computer to decompress the compressed auxiliary data and outputting quantized auxiliary data, and computer readable program code for causing the computer to generate a continuous speech signal, using the quantized feature vectors, pitch values and auxiliary data. 25. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for decoding a bit-stream representing a compressed series of acoustic vectors each containing a respective feature vector and a respective pitch value derived at a respective instance of time, each of the feature vectors having multiple components obtained by: i) deriving at successive instances of time an estimate of the spectral envelope of a digitized speech signal, ii) multiplying each estimate of the spectral envelope by a predetermined set of frequency domain window functions, wherein each window occupies a narrow range of frequencies, and computing the integral thereof, and iii) assigning said integrals or a set of predetermined functions thereof to a respective one of said components of the feature vector; said method steps comprising: (a) separating the received bit-stream into compressed feature vectors data and compressed pitch values data, (b) decompressing the compressed feature vectors data and outputting quantized feature vectors, (c) decompressing the compressed pitch values data and outputting quantized pitch values, and (d) generating a continuous speech signal, using the quantized feature vectors and pitch values. 26. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for decoding a bit-stream representing a compressed series of acoustic vectors each containing a respective feature vector, a respective pitch value and respective auxiliary data all derived at a respective instance of time, each of the feature vectors having multiple components obtained by: i) deriving at successive instances of time an estimate of the spectral envelope of a digitized speech signal, ii) multiplying each estimate of the spectral envelope by a predetermined set of frequency domain window functions, wherein each window occupies a narrow range of frequencies, and computing the integral thereof, and iii) assigning said integrals or a set of predetermined functions thereof to a respective one of said components of the feature vector; said method steps comprising: (a) separating the received bit-stream into compressed feature vectors data, compressed auxiliary data and compressed pitch values data, (b) decompressing the compressed feature vectors data and outputting quantized feature vectors, (c) decompressing the compressed pitch values data and outputting quantized pitch values, (d) decompressing the compressed auxiliary data and outputting quantized auxiliary data, and (e) generating a continuous speech signal, using the quantized feature vectors, pitch values and auxiliary data. ncrease. 17. The method of claim 15, in which the logic sub-routine generates a fault signal when the first mass flow profile exhibits a sustained decrease. 18. The method of claim 15, in which the logic sub-routine generates a fault signal when the first mass flow profile exhibits a sustained decrease while the displacement sensor indicates the control fluid valve assembly is off a null position. 19. The method of claim 15, in which the actuator further defines a second control chamber and the spool valve housing further defines a second outlet port in fluid communication with the second control chamber, the control loop further including a second outlet pressure sensor, wherein the method further comprises calculating a second mass flow rate of control fluid through the second outlet port using the inlet pressure sensor, second outlet pressure sensor, and displacement sensor to develop a second mass flow profile, and wherein the logic sub-routine generates the fault signal based on at least the first and second mass flow profiles. 20. The method of claim 19, in which the logic sub-routine generates a first chamber leak fault signal when the first mass flow profile exhibits a sustained increase and the second mass flow profile is substantially constant. 21. The method of claim 19, in which the logic sub-routine generates a piston ring fault signal when one of the first and second mass flow profiles exhibits a sustained increase and a remaining one of the first and second mass flow profiles exhibits a sustained decrease. 22. The method of claim 19, further comprising calculating a crossover pressure by averaging pressure values from the first and second outlet pressure sensors, wherein the logic sub-routine further bases the fault signal on the crossover pressure. 23. The method of claim 22, in which the logic sub-routine generates a first chamber leak fault signal when the first mass flow profile exhibits a sustained increase, the second mass flow profile is near zero, and the crossover pressure is reduced. 24. The method of claim 22, in which the logic sub-routine generates a second chamber leak fault signal when the first and second mass flow profiles are near zero and the crossover pressure is reduced. 25. A control loop for positioning a throttling element of a pneumatically operated control valve, the control loop comprising: at actuator for driving the throttling element, the actuator being disposed in an actuator housing defining at least a first control chamber; a positioner including second stage pneumatics having a housing defining an inlet port in fluid communication with a control fluid supply and a first outlet port in fluid communication with the first control chamber, and a control fluid valve assembly disposed in the housing for controlling flow of control fluid from the inlet port to the first outlet part; an inlet pressure sensor in fluid communication with the housing inlet port for measuring an inlet port pressure; a first outlet pressure sensor in fluid communication with the first outlet port for measuring a first outlet port pressure; a displacement sensor for determining a control fluid valve assembly position; and a diagnostics unit communicatively coupled to the inlet pressure sensor, first out