WO1997009678A1 - Systeme de diagnostic d'organes biologiques par utilisation d'un reseau neuronal reconnaissant une erreur aleatoire d'entree - Google Patents

Systeme de diagnostic d'organes biologiques par utilisation d'un reseau neuronal reconnaissant une erreur aleatoire d'entree Download PDF

Info

Publication number
WO1997009678A1
WO1997009678A1 PCT/US1996/013498 US9613498W WO9709678A1 WO 1997009678 A1 WO1997009678 A1 WO 1997009678A1 US 9613498 W US9613498 W US 9613498W WO 9709678 A1 WO9709678 A1 WO 9709678A1
Authority
WO
WIPO (PCT)
Prior art keywords
neural network
parameter data
data
network system
parameter
Prior art date
Application number
PCT/US1996/013498
Other languages
English (en)
Inventor
Joseph P. Laurino
Original Assignee
The Memorial Hospital
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by The Memorial Hospital filed Critical The Memorial Hospital
Publication of WO1997009678A1 publication Critical patent/WO1997009678A1/fr

Links

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis

Definitions

  • the present invention relates to the application of neural networks for analyzing and/or diagnosing the condition of an object or function.
  • a system of neural networks is used to not only analyze the object or function, but also to determine whether errors exist in the data used by the networks in their analyses.
  • the invention relates to a system that incorporates a neural network system for analyzing and diagnosing the condition of a biological organ or body function.
  • the neural network system is designed to recognize the presence of error in measurement data from automated measuring devices or from manually- performed laboratory tests.
  • a biological organ as used herein refers to any functional component in the body. Examples are the heart, the liver, and the kidneys.
  • a body function is any chemical process or operation occurring in the body, such as blood flow, digestion, electrolyte production, and respiration, to name a few.
  • measurements are taken of their physical, chemical and/or physiological characteristics such as those described above. These measurements may be obtained using automated measuring devices, or manual laboratory techniques. Very often, however, such measurements require that a large number of samples be tested. Also, laboratories will conduct such measurements with samples from a large number of patients together. Further, the results of a large number of measurements may be analyzed by laboratory personnel together. As one may readily appreciate, the analysis of various measurements can be both highly complex and time consuming, especially when measurements are taken and analyzed in large numbers. Various attempts to optimize the procedures for taking and analyzing measurements have often suggested the use of artificial or computer intelligence as a means of making such procedures more efficient. However, the question arises as to which type of computer intelligence should be used, i.e. expert systems, fuzzy logic systems, or neural networks?
  • neural networks are particularly capable in using known information to draw conclusions about things that are similar to but not exactly like the known information. Analyses that require generalizations, the understanding of subtle relationships, and the processing of large amounts of data are among the applications to which neural networks are well-suited.
  • neural networks are used in numerous fields including financial forecasting, business decision making, pattern/character recognition, mechanical controls, and medical diagnosis.
  • Neural networks as they are known today consist of a plurality of artificial or simulated neurons that are interconnected to one another. Each neuron generally consists of a number of inputs that enter a summation and transfer element which then generates an output based on the inputs received. Each neuron is connected to other neurons in order to either receive inputs from or output data to other neurons. This interconnection between neurons is generally organized in at least three layers. An input layer receives the information that the network will use to generate an answer. The output layer produces the final answer. A hidden intermediate layer associates and connects the input layer with the output layer.
  • the architecture of a neural network is developed through training in which different sets of data inputs and their associated known outputs are used to generate the mathematical models for the connections between neurons. Details on the development of a neural network's architecture as known in the art are discussed in the publication Introduction to Neural Networks: Design, Theory and Applications. J. Lawrence,
  • the prior art involving the determination of random error involves the interpretation of repeated measurements and/or observations of single quantity. By conducting multiple measurements of a single quantity, a range of values randomly scattered around a true value will be obtained. This data set involving a single measured quantity is amenable to statistical treatment in order to determine the presence of random error in some or all of the measured values comprising the data set.
  • the prior art does not allow for the determination of random error in a single measured value, but rather relies on the statistical analysis of repeated determinations of a single quantity.
  • determining random error one currently constructs a plot or graph of all measurements or observations taken of such a single quantity.
  • the plotted measurements or observations form a "population" of data that is used to construct a histogram, also called a frequency diagram or frequency distribution.
  • the histogram shows the frequency with which a particular value or range of values is obtained versus the scale of all values obtained for a single quantity.
  • the shape of this curve is known as a "normal” or "Gaussian” distribution for this quantity.
  • the curve is defined by the average of the values (the "central tendency") and the range of these values (the "dispersion").
  • the most useful measurement of dispersion is given by a quantity known as the "variance". After constructing such a mathematical curve, one is able to determine the probability that a subsequent measurement of that value is contaminated by random error by comparing that measurement to the graph. If the measurement falls within the range of the plotted points (as defined by the central tendency and the variance), it is determined to be a valid measurement. If, on the other hand, the measurement falls outside the range of plotted points, it is considered to be erroneous. Thus, it is evident that the prior art requires multiple measurements of a single quantity in order to statistically determined the probability of random error contamination. The present invention is directed to overcoming the deficiencies of the prior art as described above.
  • the present invention is generally directed to a system using a neural network to analyze parameter data that describes the condition of an object or system being studied.
  • the neural network is designed and trained to recognize systematic and random errors that may occur when the parameter data is obtained. By recognizing systematic and random errors, steps can be taken to eliminate the error and correct the parameter data. It is this ability to recognize error, both systematic and random, which distinguishes the present invention from all previously existing methods and systems, as will be discussed below.
  • the present invention in a specific embodiment is directed to a system for analyzing the condition of biological organs based on a set of parameters that have been identified as physiologically related factors that will determine a diagnosis involving a particular organ.
  • a neural network is trained and used to analyze the values of these parameters as they relate to each other, and to first determine whether there is an error in the input data, and if not, then to generate a diagnosis from its analysis.
  • an initial set of training data consisting of a set of values representing parameter data and a corresponding output answer
  • each set of parameter data and its corresponding output answer will be referred to as an input/output data pair.
  • This set of training input/output data pairs includes input/output data pairs having combinations of values for the parameters that will generate the different types of diagnoses associated with any one organ.
  • each input/output data pair consists of parameter values (or inputs) that together result in a given diagnosis (or output).
  • this training set includes input/output data pairs of parameter values that are designed to simulate input data errors.
  • Such errors include, for example, an incorrect temperature reading, improper test sample identity (i.e., patient sample mix-up), a miscalibrated measuring/testing instrument, or a contaminated test sample.
  • one or more of the parameter values in a given input/output data pair may be set above or below the range of values possible for those parameters.
  • one or more parameter values in an error input/output data pair may be set to values that are clearly erroneous relative to the other parameter values in that error input/output data pair.
  • the outputs of such error input/output data pairs all designate "ERROR.”
  • the neural network can either output an indication of whether the parameter data is "correct", i.e. all parameter values represent accurately obtained measurements or test results, or "corrupted ' i.e. containing at least one parameter that is erroneous.
  • the neural network can, as above, output either a diagnosis of the condition of the organ whose parameter values are being analyzed, or again output an indication that the parameter data is corrupted, but then, when the neural network concludes that an error exists among the parameter data, the system can proceed to initiate an analysis to isolate and identify the parameter(s) that is the source of the error.
  • One method for analyzing the parameter data is to generate different versions of the neural network for the particular organ, but with one or more parameters less than the original neural network associated with that organ. The different neural networks then each conduct their own analysis of the input data, but based on their more limited sets of parameters.
  • the system can identify which parameter or parameters are erroneous.
  • the present invention is capable of identifying erroneous input data without merely comparing the data to an expected range. Rather, a novel aspect of the present invention is its ability to detect the probability of random error contamination in a measurement from a single measurement and/or observation without the need to construct a normal or Gaussian distribution of the data.
  • the present invention allows a user to take corrective action specific to the erroneous parameter or parameters.
  • further analysis of a few or even one parameter to simply determine the source of error such as analyzing the measuring equipment that generated those parameters, the conditions under which the measurements of the parameters were made, and/or the test samples from which the parameters were derived, can be complex and time-consuming.
  • the actual corrective actions to eliminate the errors such as repeating the tests or measurements, recalibrating and/or repairing faulty instruments, instituting measures to prevent corruption or decaying of the test samples, changing current procedures that cause the error(s), and developing new procedures that avoid the error(s) can be as complex and time-consuming, if not more so.
  • the user instead of having to analyze every parameter to determine the source of error, the user can focus any further analysis to the erroneous parameter (s), thereby optimizing the time, effort and expense put to correcting the error.
  • Figure 1 A shows a general block diagram of the system for implementing the first and second embodiments of the present invention
  • Figure IB shows a general block diagram of the components of the central data processing device of the present invention.
  • Figure IC shows a general block diagram of the components of the neural network system of the present invention
  • Figure 2 shows a flowchart-style diagram illustrating the training of the neural networks to recognize parameters for both correct diagnoses and error conditions
  • Figure 3 shows a flowchart-style diagram illustrating a routine for testing the neural networks of the present invention
  • Figure 4A shows a flowchart-style diagram illustrating the general operation of the neural network of the present invention according to a first embodiment thereof;
  • Figure 4B shows a flowchart-style diagram illustrating the general operation of the neural network including the process for identifying the source of an error condition determined by the neural network according to a second embodiment of the present invention
  • Figure 5 shows a general block diagram of the system for implementing a third embodiment of the present invention
  • Figure 6 shows a general block diagram of the system for implementing a fourth embodiment of the present invention
  • Figure 7A shows a general block diagram of the system for implementing a fifth embodiment of the present invention
  • Figure 7B shows a general block diagram of the system for implementing a variation of the fifth embodiment of the present invention
  • Figure 8A shows a general block diagram of the system for implementing a sixth embodiment of the present invention.
  • Figure 8B shows a general block diagram of the system for implementing a variation of the sixth embodiment of the present invention.
  • the present invention is implemented in a system 1 that incorporates a central data processing device 3 connected to a plurality of automated measuring instruments 2.
  • the central data processing device 3 is implemented either through a mainframe computer such as an IBM AS/400, or a network server computer using, for example, an IBM-compatible computer that has an Intel Pentium processor.
  • measurement data generated by the measuring instruments is received by a central processing unit 7 that can then store the measurement data in a data memory bank 8.
  • the data memory bank 8 is embodied primarily as storage space on a non-volatile memory device that can be written into, such as a hard disk or magnetic tape. By using a non- volatile memory device, measurement data can be stored over an extended period of time, allowing the use of the measurement data in an analysis that is repeated several times over or in several different analyses.
  • the central processing unit 7 can also access other data memory banks 9 for additional data that may be applicable. Such information would include patient hospital records, test data from prior measurements and tests, measurement data for other diagnosis for the same patient, and calculations of parameters derived from other measurement data.
  • the other data memory banks 9 are also embodied in a non- volatile memory device, such as a hard disk or magnetic tape. However, since data in the data memory banks 9 can include patient hospital records or other valuable information, the data memory banks 9 can include non- volatile memory devices, i.e. hard disks or magnetic tapes, that are protected by limited access procedures or that cannot be overwritten. An example of a memory device that cannot be overwritten is a CD-ROM.
  • the measuring instruments 2 are used to conduct automated laboratory tests on analytes related to a biological organ or body function. As noted above, the measuring instruments 2 then generate measurement data based on the laboratory tests conducted.
  • the measurement data is inputted into the central data processing device 3.
  • the number I of measuring instruments 2 is determined by the number and types of parameters being measured or tested.
  • the measuring instruments 2 would be used to measure the level of the thyroid stimulating hormone (TSH), the level of total thyroxine (T4), the level of free thyroxine (T4 Free), the level of triiodothyronine (T3) and the level of triiodothyronine uptake (T3 Uptake), and to determine the free thyroxine index (FTI) based on the level of the total thyroxine (T4) and the level of triiodothyronine uptake (T3 Uptake).
  • TSH thyroid stimulating hormone
  • T4 Free the level of free thyroxine
  • T3 Uptake the level of triiodothyronine uptake
  • FTI free thyroxine index
  • Examples of measuring instruments 2 for perfo ⁇ ning such tests include a Dade
  • Stratus Ilntellect device a DPC "Coat-A-Count” RIA assay, a DCP Immulite device, an ABBOTT Axiom-IMx device, a BAYER Immuno I device, Behring Opus-Opus Plus- Magnum device, a Corning ACS 180 device, A Boehringer Mannheim ES-300 device, a TOSOH AIA 600, 1200 device, and a Hybritech Photon ERA device. These devices are examples of radioimmunoassay and enzymeimmunoassay systems for analyzing thyroid functions.
  • the Dade Stratus Ilntellect device can measure the TSH, T4, T3 and T3 Uptake levels, and calculate the FTI.
  • the DPC "Coat-A-Count" RIA assay is used to measure the Free T4 level. All the other devices listed above are all capable of measuring the TSH, T4, Free T4, T3, and T3 Uptake levels, and calculating the FTI.
  • Measurement data is also inputted into the central data processing device 3 using a manual input terminal 6.
  • the manual input terminal 6 is embodied in a terminal connected to the central data processing device 3 when implemented as a mainframe computer, or in a stand-alone computer (i.e., an IBM-compatible personal computer) networked with the central data processing device 3 when implemented as a network server.
  • the manual input terminal 6 can even be implemented in a portable or laptop computer that communicates with the central data processing device 3 through a modem or a dedicated network docking port.
  • the manual input terminal 6 is intended to allow a user to input measurement data derived from having conducted measurements in a laboratory or in a remote location away from the central data processing device 3 using measuring instruments or non-automated test equipment not connected with the central data processing device 3.
  • the central processing device 3 incorporates interface devices 10 for interfacing with the various measuring instruments 2.
  • Automated measuring instruments such as those specifically listed above, will output data signals using signal communication formats initially selected by the manufacturer, for example, ASCII format.
  • the interface devices 10 translate the data signals from the measuring instruments 2 into a form usable by the central data processing device 3.
  • One example of an interface device is a Beckman Synchron CX4CE/CX7 system that is used to interface the central data processing device 3 with a measuring instrument 2 using RS-232-C interface hardware and X-ON, X-OFF communication protocols.
  • Another example is the software- implemented ILS-5 Laboratory System by Dynamic Healthcare Technologies, Inc.
  • the ILS-5 system interfaces a mainframe computer, e.g. an IBM AS/400, with the various automated measuring instruments.
  • a neural network system 4 is connected to the central data processing device 3.
  • the central data processing device 3 communicates with the neural network system 4 by providing the measurement data for the neural network system 4.
  • the central processing unit 7 can either access and output the measurement data directly from the data memory banks 8, or process the measurement data into parameter data that is in a form usable by the neural network system 4.
  • the central data processing device 3 can also calculate other parameter data from the measurement data for the neural network system 4.
  • the FTI is calculated as follows:
  • the neural network system 4 receives the measurement data, and processes that data in accordance with its specific design and training, and the specific application selected.
  • the neural network system 4 itself is the implementation of several sets of neural networks. Each set of networks is designed to analyze a specific biological organ or body function. Each neural network in a set is designed to analyze the same data on the biological organ or body function, except in a slightly different manner from each other. For example, in addition to the thyroid, other body organs and/or functions can be analyzed using a set of neural networks so designed. Among such other body organs are the liver, the kidney, tumors, and the heart. Examples of body functions are electrolytes, blood gases and fertility.
  • a neural network system 4 consisting of neural networks designed to analyze the thyroid are used to process the measurement data obtained from the measuring instruments 2.
  • the measurement data is inputted into the neural network system 4, and then the network system 4 outputs signals indicating its diagnosis or answer.
  • the signals on the diagnosis of the neural network system 4 are sent to an output or display device 5.
  • the neural network system 4 is implemented as a set of computer-simulated networks generated using a neural network simulation software tool, for example, "BrainMaker," Version 3J by California Scientific Software.
  • the neural network system 4 can also be implemented as a series of hard-wired neural networks.
  • the computer-simulated implementation of the neural network system 4 is preferred.
  • the neural network system 4 shown in Figure IC consists of several neural network versions 4a.
  • the neural network system 4 is embodied in a computer or other data processing device, such as an IBM-compatible personal computer.
  • the neural network system 4 can be implemented as a stand-alone unit separate from the central data processing device 3, or as part of the central data processing device 3 itself, i.e. a software or hardware module thereof. Consequently, actual operation of the neural network system 4 can be controlled through that computer using an operator interface 11.
  • the operator interface 11 is embodied in the computer's terminal that includes a keyboard and a display. The display of the operator interface 11 can serve as the output device 5 of Figure IA.
  • the number X of neural network versions 4a is based on the number of characteristics measured by the all measuring instruments 2 and by non-automated tests that are reported to the central data processing device 3 through the manual input terminal 6.
  • a first neural network version 4a that analyzes the biological organ or body function using every identified characteristic or parameter. That number N of identified characteristics or parameters, hereinafter referred to as "parameters,” constitutes the maximum number of parameters for that biological organ or body function.
  • All other network versions 4a after the first network version will be designed to analyze the biological organ or body function using subsets of the maximum number N. The subsets are permutations of the different parameters with the number of parameters being less than the maximum number of parameters N.
  • the additional network versions would include versions using different permutations of parameters less than six. Specifically, additional network versions include those using permutations of five of the maximum six parameters, permutations of four of the maximum six parameters, permutations of three, etc.
  • Table 1 shows examples of some of the permutations of parameters for several of the network versions for analyzing a thyroid. In Table 1, the symbol "Used” designates that the parameter is used by the network version; the symbol " * " indicates that the parameter is not used. As shown, the first network version uses all six of the parameters TSH, T4, Free T4, T3, T3 Uptake and FTI.
  • a second network version uses five of the six parameters — T4, Free T4, T3, T3 Uptake and FTI; while a third network version uses five different parameters - TSH, Free T4, T3, T3 Uptake and FTI. Additional network versions are created using permutations of progressively fewer parameters. In this example of the parameters for diagnosing a thyroid condition, a total of 121 network versions are generated using all permutations of the six original parameters.
  • the neural network system 4 of the present invention can produce viable diagnoses even when measurement data on the biological organ or body function is limited.
  • Each set of neural networks for analyzing a specific biological organ or body function is generated by training each of the neural network versions in the set.
  • different sets of parameter input data and their co ⁇ esponding output answers are inputted into a neural network.
  • the neural network is then allowed to develop the necessary internal architecture based on those sets of parameter data.
  • An initial set of training input/output data pairs is developed.
  • This set of training input/output data pairs uses selected parameter values that together translate into predetermined outputs.
  • One way of generating the training set is to use values for each of the parameters that are considered within normal ranges for those parameters at a given output. More specifically, data pairs can be generated using permutations of the different parameters within their normal ranges.
  • the normal value range of T4 is 5-12 ⁇ g/dl (micrograms per decaliter), while the normal value range of T3 is 100-200 ng/dl (nanograms per decaliter).
  • T4 is set at a value within its normal range (e.g., 5 ⁇ g/dl) in combination with selected values for T3 within its normal range. Another set of data pairs would have T4 set at another value within its normal range (e.g., 6 ⁇ g/dl) again in combination with selected values for T3 with its normal range. Further sets of data pairs can be generated using similar permutations and combinations of the TSH, T4, Free T4, T3, T3 Uptake, and FTI parameters with corresponding indications of either a euthyroid, hyperthyroid or hypothyroid condition as their outputs.
  • An alternative method for generating an initial training set would be to use parameter values and corresponding outputs from actual cases.
  • data pairs representing the euthyroid, hyperthyroid and hypothyroid conditions can be easily be provided.
  • the process of training a neural network includes the inputting of the training set of input/output data pairs.
  • two types of input/output data pairs those that will output a correct diagnosis (Step 101), and those that will output an error condition (Step 102).
  • the data pairs with correct diagnoses can be obtained by either generating different combinations and permutations of parameter values or by using parameter values form actual cases.
  • Example values for input/output data pairs used in analyzing a thyroid condition are shown in Table 2. As shown, twenty-five input/output data pairs for correct diagnoses were generated for training a neural network, where nine data pairs indicated the euthyroid condition, eight data pairs indicated the hyperthyroid condition, and eight data pairs indicated the hypothyroid condition. Permutations of input/output data pairs can also be used to generate different combinations of the TSH, T4, Free T4, T3, T3 Uptake, and FTI parameters that have co ⁇ esponding indications of "ERROR” as the output. Similarly, actual cases of data pairs that would indicate "ERROR” can be used. In Table 2, input/output data pairs indicating "ERROR” constituted an additional twenty-five data pairs for training the network.
  • the total number of input/output data pairs for training should initially be divided in approximately equal numbers between the different types of outputs possible.
  • the initial number of data pairs will vary as required by the training of the neural network.
  • the initial set of data pairs is inputted to develop and train the neural network architecture.
  • the neural network begins connecting and weighting its input, output and hidden neurons based on the inputted data pairs. This process of connecting and weighting of neurons constitutes the development of the network's architecture.
  • the training and development of each neural network in this manner is consistent with techniques for developing neural network architectures as known in the art.
  • the network stops and a determination is made whether the network is fully trained (Step 104).
  • the operation of the neural network i.e. its ability to output an expected answer using the training set of input/output data pairs, is evaluated by inputting the training set into the neural network as if the neural network is analyzing parameter values for an actual case. If the network outputs all the expected answers, then the network is tested using a set of non-training input/output data pairs (Step 105).
  • the training process evaluates the incorrect responses of the network (Step 106). Specifically, the data pairs that the network analyzed incorrectly are identified, and then analyzed to determine what are the common characteristics of those data pairs. For example, if the network incorrectly analyzed all data pairs having outputs indicating a euthyroid condition, this means that the network does not yet fully comprehend the conditions that will result in the euthyroid condition. This further indicates that not enough cases were used in the initial training set (Step 107).
  • the network is deficient in analyzing the euthyroid condition. Therefore, at Step 109, the training process proceeds to Step 110, where additional data pairs that indicate the euthyroid condition are added to the training set. If, on the other hand, the network was deficient in analyzing the "ERROR" condition, then at Step 111, additional data pairs indicating the "ERROR” condition are added.
  • the exact number of data pairs to be added and/or the values of the parameters in the data pairs to be added to the training set is the number required to train the network, as further illustrated below.
  • the network fails to analyze any of the data pairs indicating a euthyroid condition correctly, then for example, enough data pairs may be added to sharply increase the total number of such data pairs in the training set (e.g., double, triple). Alternatively, a smaller number of data pairs with parameter values more representative of the euthyroid condition may be selected instead.
  • a thorough understanding of the neural network's progress in training, and even trial and error, will most likely guide the user on how to correct the network.
  • the network inco ⁇ ectly analyzes only a subset of the data pairs that indicate a euthyroid condition, and that have parameter values close to or common with one another, then this indicates that the number of data pairs in the training set is sufficient. Instead, the training requirements imposed on the network will require adjustment. For example, the range of values that the network is allowed to consider for a particular parameter can be truncated. The amount of time that the network is given for analyzing a data pair can be increased so that the network can better analyze the data pair. Also, the order in which the data pairs in the training set are presented to the network can be randomized, to discourage the network from relying on the order of the data pairs as a means for analyzing the data pairs.
  • Step 103 After the training requirements are adjusted or further input/output data pairs are added (Steps 108, 110 or 111), additional training takes place and the neural network architecture is again developed (Step 103).
  • Step 105 for testing is initiated instead ofthe incorrect response evaluation (Step 106), then as shown in Figure 3, the process for testing the neural network (Process 200) incorporates the step of inputting a non-training set of input/output data pairs into the network (Steps 201, 202). Like the training set, the data pairs in this set have predetermined outputs. Those predetermined outputs are compared with the actual analyses of the network. If the network correctly analyzes all the data pairs, then training is considered complete, and the neural network can be applied to analyzing input of actual cases (Step 205). Otherwise, if the network fails to analyze all the data pairs correctly, then the network must be re-trained (Step 204). Re-training of the network is a repeat of the initial training process of the network as illustrated in Figure 2. Unlike the initial training process, the training set must be modified by adding new data pairs, and/or the training requirements must be adjusted as was done in Step 108.
  • a main feature of the present invention is the use of input/output data pairs that have outputs indicating "ERROR.” Unlike the prior art, the use of input/output data pairs indicating error conditions is not intended to train the network to avoid outputting incorrect diagnoses. Rather, the use of error condition input/output data pairs allows the neural network to detect the presence of errors in the parameter data being inputted. Input/output data pairs for indicating "ERROR" are used to simulate both the systematic and random errors that may occur when measurements are taken or when tests are conducted. Random errors include an incorrect reading of an instrument by a technician, contaminated test samples, or decayed test samples. Examples of systematic enors include too few test samples taken, and miscalibrated measuring/testing instruments.
  • the data pairs for indicating "ERROR” are derived from setting at least one parameter in each such data pair at an inco ⁇ ect value.
  • inco ⁇ ect values include those that are too high or too low for the specific parameter, or those that are outside the range of values possible for that parameter relative to the value of another related parameter.
  • T3 triiodothyronine
  • T4 total thyroxine
  • data streams from measuring instruments 2 are inputted into the central data processing device 3 (See Figure IA).
  • the data streams are translated into data usable by the central data processing device 3 by the interface devices 10 (See Figure IB).
  • the central processing unit 7 stores the data in a measurement data memory bank 8 directly, or manipulates that data into parameter data usable by the neural network system 4.
  • the central processing unit 7 also calculates and derives other parameter data from the data received from the measuring instruments 2.
  • FIG. 4A illustrates the process of the neural network (Process 300) of the present invention in the first embodiment. As shown, input data of an actual case are inputted into and processed by the trained network (Process 301, Step 302). The network then generates an output based on the inputted parameters (Process 303).
  • Step 304 a determination is made whether the output is an indication of "ERROR” or a diagnosis (Step 304). If the output is an analysis of the biological organ or body function, then it is reported (Step 305) through an output device 5 (See Figure IA).
  • an output device 5 include a display on a computer terminal (See Figure IC, Reference 11), a paper printout, or an alarm.
  • the reporting of an error condition initiates a process of analysis to determine which parameter or parameters are the source of the error (Process 307).
  • the parameter data from the actual case is divided into subsets based on the various types of network versions available for analyzing that organ or function (Step 308).
  • Table 1 illustrates 10 permutations of a possible 121 permutations of subsets using the parameters for the analysis of thyroid conditions for determining the subsets.
  • an "ERROR" indication results when the neural network using all six parameters TSH, T4, Free T4, T3, T3 Uptake and FTI (Network Version 1) is used to analyze an actual case.
  • Network Versions 2 through 7 will be used to determine the source of error in the measurement data. As shown in Table 1, Network Versions 2 through 7 differ from each other by one parameter. Specifically, Network Version 2 uses the T4, Free T4, T3, T3 Uptake and FTI parameters, while Network Version 3 uses the TSH, Free T4, T3, T3 Uptake and FTI parameters, etc.
  • Each of the subsets of parameter data is then applied in a Process 309 in which the subsets are inputted into and processed by the appropriate network versions that use a particular subset. Each network version subsequently generates an output (Step 310).
  • the outputs from all the network versions are then compared with one another to determine the parameter or parameters that are erroneous (Step 311).
  • the system identifies which parameter or parameters are erroneous, and indicates the source of e ⁇ or that caused the "ERROR" indication in the initial analysis (Step 312).
  • the number of parameters used in any of the network versions is expressed as follows: (N - m) where m is the number of parameters not considered and m > 1 As noted above and illustrated in Table 1, the network versions other than
  • the maximum number of parameters N for a given biological organ or body function should not be less than three (N > 3).
  • network versions that used only one less parameter than the maximum number N
  • other network versions can be generated with combinations of two or more parameters are removed from consideration. These other network versions are based on removing parameters that are inter-related or affect each other's values. Specifically, some parameters are either directly related to other parameters. Some parameters may even be directly derived from other parameters. For example, in many applications and technologies, temperature is proportional to pressure, weight is proportional to mass, and velocity is proportional to acceleration. Parameters such as those described are so related that when one parameter is removed from consideration, the presence or absence of other related parameters does not affect the overall analysis.
  • network versions can be generated that have all such parameters removed together from consideration.
  • T3 triiodothyronine
  • T4 total thyroxine
  • one network version 4a (See Figure IC) has the T3 and T4 parameters removed; that particular network version 4a only considers the parameters of the thyroid stimulating hormone (TSH), the measurement of free thyroxine (T4 Free), the measurement triiodothyronine uptake (T3 Uptake), and the test for the free thyroxine index (FTI).
  • TSH thyroid stimulating hormone
  • T4 Free measurement of free thyroxine
  • T3 Uptake the measurement triiodothyronine uptake
  • FTI free thyroxine index
  • the neural network system 4 is described as being implemented, among other ways, as a computer or data processing system either separate from or integral with the central data processing device 3.
  • the data from the measuring instruments 2 are inputted into the central data processing device 3 for storage or further processing for the neural network system 4.
  • each of the measuring instruments 2 is connected to separate neural network systems 40. The outputs of the separate network systems 40 are then inputted into the central data processing device 3.
  • the central data processing device 3 uses the interface devices 10 (See Figure IB) to convert the outputs of the separate network systems 40 into a usable form, and then stores all the converted outputs in its measurement data memory bank 8, further processes the outputs into parameter data before storage, or reports the outputs from the separate network systems 40.
  • the separate network systems 40 can be implemented as either computer(i.e., software)-generated or hard-wired systems. As computer-generated systems, the separate network systems 40 can be further implemented as separate computers or data processing devices, or as separate neural network systems 40 on the same computer or data processing device.
  • Each of the measuring instruments 2 is designed to generate data for a plurality of parameters (i.e., N > 3), whereby their corresponding neural network systems 40 can generate the necessary architectures during training.
  • the manual input terminal 6 is used to input a plurality of parameter data so that its corresponding neural network system 40 can generate the necessary architecture during its training.
  • Each of the measuring instruments inputs data 2 via an interface device 41 to a corresponding neural network system 40.
  • the manual input terminal 6 is also connected via an interface device 41 to a co ⁇ esponding neural network 40.
  • the interface device 41 converts the data from the measuring instruments 2 and the manual input terminal 6 into parameter data usable by the neural network systems 40.
  • the interface device 41 can be implemented as either software or hardware equivalent to the devices implementing the interface devices 10 in the first and second embodiments of the present invention discussed above.
  • each of the measuring instruments 2 separately outputs measurement data 50.
  • Each of the measuring instruments 2 represents an automated measuring device that is not connected to the central data processing device 3, but that is used to generate and output measurement data 50.
  • the measurement data 50 from each of the instruments 2 is inputted through a manual input terminal 51 into the central data processing device 3.
  • Manual measurements and testing 2a done using non-automated instruments and laboratory procedures to generate measurement data 50 can also be inputted into the central data processing device 3 using a manual input terminal 51.
  • the manual input terminals 51 can be implemented as terminals to the central data processing device 3 implemented as a mainframe computer, or as stand-alone personal computers communicating with the central data processing device 3 implemented as a network server. When implemented as stand-alone personal computers, the manual input terminals 51 after receiving the measurement data 50 can process it, such as by calculating other parameter data from the measurement data 50 or by converting it into a data format usable by either the central data processing device 3 or the neural network system 4. The manual input terminals 51 can even store the measurement data 50.
  • the measurement data 50 and other parameter data from the manual input terminals can be stored in the measurement data memory bank 8 or be further processed into a form usable by the neural network system 4 before storage.
  • the neural network system 4 accesses the central data processing device 3 for all the parameter data needed.
  • the neural network system 4 initiates its analysis and accesses the memory banks 8 of the central data processing device 3 based on a signal from the central data processing device 3, or from a user.
  • each of the measuring instruments 2 representing an automated measuring device not connected to the central data processing device 3, separately outputs measurement data 60.
  • the measurement data 60 from each of the measuring instruments 2 is inputted through a manual input terminal 61 into the neural network system 4.
  • Manual measurements and testing 2a done using non-automated instruments and laboratory procedures to generate measurement data 60 can also be inputted into the neural network system 4 through a manual input terminal 61.
  • the manual input terminals 61 can be implemented as terminals to the neural network system 4 implemented as a mainframe ' 5 or stand-alone computer.
  • the manual input terminals 61 can be implemented as stand-alone personal computers communicating with the neural network system 3.
  • the manual input terminals 61 after receiving the measurement data 60 can process it, such as by calculating other parameter data from the measurement data 60 or by converting it into
  • the manual input terminals 61 can also store the measurement data 60.
  • the outputs generated by the neural network system 4 are inputted into the central data processing device 3 either manually using a separate manual input terminal connected to the central data processing device 3, or through an interface device between
  • the outputs of the neural network system 4 are outputted through the output device 5 connected to the central data processing device 3.
  • the 20 2a are connected via interface devices 62 to the neural network system 4. Data from the measuring instruments 2 is therefore inputted directly into the neural network system 4, instead of through the manual input terminals 61.
  • the interface devices 62 convert the data from the measuring instruments 2 into a form usable by the neural network system 4.
  • the interface devices 62 can also be implemented as software or hardware equivalent
  • the measurement data 70 from each of the instruments 2 is inputted through a manual input terminal 71 into the neural network system 4.
  • Manual measurements and testing 2a done using non-automated instruments and laboratory procedures to generate measurement data 70 can also be inputted into the neural network system 4 through a manual input terminal 71.
  • the manual input terminals 71 process the measurement data 70. For example, after receiving the measurement data 70, the manual input terminals 71 can calculate other parameter data from the measurement data 70, and convert the data into a form usable by the neural network system 4.
  • the outputs generated by the neural network system 4 are outputted through the output device 5 connected to the computer or data processing device implementing the neural network system 4.
  • the measuring instruments 2 and the manual input terminal 71 for the manual measurement and testing 2a are connected via interface devices 72 to the neural network system 4. Data from the measuring instruments 2 is therefore inputted directly into the neural network system 4, instead of through the manual input terminals 71.
  • the interface devices 72 convert the data from the measuring instruments 2 into a form usable by the neural network system
  • the interface devices 72 can be implemented as software or hardware equivalent to the devices used to implement the interface devices 10 in the first and second embodiments described above.
  • the output device 5 of the invention can be embodied in a device for translating the output of the neural network system 4 into control signals.
  • control signals can include signals for controlling the measuring instruments 2 to repeat the tests or initiate new tests, signals controlling equipment monitoring a patient to output current readings or to change their current monitoring settings, signals controlling medication-administering equipment, or signals controlling communicating with diagnostic systems for generating treatment procedures for a patient.
  • the system of the present invention is applicable to different fields of endeavor outside of the medical field as described above. For example, the system of the invention would be applicable to the field of automated manufacturing machines.
  • the system may be used to monitor various machines in an automated assembly line, or even to monitor a device or chemical being manufactured.
  • parameter data is measured or taken to monitor the machine and thereby maintain the machine at optimum performance.
  • parameter data is measured or taken to insure that the device or chemical is being assembled properly.
  • Other examples of where the system of the present invention may be applied include the use of the system to monitor the operation of a car, aircraft, ships, robots or spacecraft over the lifetime of the object.
  • Parameter data can be measured or taken on a periodic basis to monitor the operation and performance of the object.

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Medical Informatics (AREA)
  • Public Health (AREA)
  • General Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Theoretical Computer Science (AREA)
  • Epidemiology (AREA)
  • Primary Health Care (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Computing Systems (AREA)
  • Artificial Intelligence (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Evolutionary Computation (AREA)
  • Molecular Biology (AREA)
  • Pathology (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Databases & Information Systems (AREA)
  • Testing Or Calibration Of Command Recording Devices (AREA)

Abstract

La présente invention concerne un système (1) destiné à l'analyse de l'état d'organes biologiques à partir d'un jeu de paramètres identifiés comme facteurs déterminants pour le diagnostic portant sur un organe particulier. Un réseau neuronal (4) utilisé pour analyser les relations entre les valeurs de ces paramètres après avoir subi un apprentissage à cette fin, détermine dans un premier temps s'il y a une erreur dans l'entrée, et sinon, génère dans un second temps un diagnostic à partir de l'analyse. Pour l'apprentissage du réseau neuronal (4), on fournit en entrée au réseau neuronal (4), d'une part un jeu initial de couples entrée/sortie (101, 102) simulant les paramètres de diagnostic 'valide', et d'autre part des combinaisons de paramètres d'erreur. Lorsqu'on lui fournit des données paramètres destinées à la production d'un diagnostic réel, le réseau neuronal (4) est capable, soit d'indiquer qu'une erreur est susceptible de s'être produite dans les mesures ou les résultats de tests obtenus comme données d'entrée, soit de diagnostiquer comme 'valide', dans les limites d'une marge de tolérance spécifique, les conditions concernant l'organe.
PCT/US1996/013498 1995-09-01 1996-08-26 Systeme de diagnostic d'organes biologiques par utilisation d'un reseau neuronal reconnaissant une erreur aleatoire d'entree WO1997009678A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US52292895A 1995-09-01 1995-09-01
US08/522,928 1995-09-01

Publications (1)

Publication Number Publication Date
WO1997009678A1 true WO1997009678A1 (fr) 1997-03-13

Family

ID=24082949

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US1996/013498 WO1997009678A1 (fr) 1995-09-01 1996-08-26 Systeme de diagnostic d'organes biologiques par utilisation d'un reseau neuronal reconnaissant une erreur aleatoire d'entree

Country Status (1)

Country Link
WO (1) WO1997009678A1 (fr)

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1999009507A1 (fr) * 1997-08-14 1999-02-25 Adeza Biomedical Corporation Procedes permettant de selectionner, developper et ameliorer des tests diagnostiques d'etats lies a la grossesse
WO1999035602A1 (fr) * 1998-01-05 1999-07-15 Biosite Diagnostics, Inc. Methodes de controle de dosages
EP0962186A1 (fr) * 1998-06-02 1999-12-08 Bayer Corporation Système de diagnostic automatisé pour mettre en oeuvre des dosages immunologiques et des dosages de chimie-cliniques selon un algorithme réflexe
GB2405203A (en) * 2003-08-22 2005-02-23 Dennis Sunga Fernandez Biosensor and simulation system
GB2429544A (en) * 2005-08-22 2007-02-28 Mv Res Ltd A classification system for recognising mis-labelled reference images
US7713703B1 (en) 2000-11-13 2010-05-11 Biosite, Inc. Methods for monitoring the status of assays and immunoassays
US9384323B1 (en) 2002-10-18 2016-07-05 Dennis S. Fernandez Pharmaco-genomic mutation labeling

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5285523A (en) * 1990-09-25 1994-02-08 Nissan Motor Co., Ltd. Apparatus for recognizing driving environment of vehicle
US5303328A (en) * 1990-10-24 1994-04-12 Hitachi, Ltd. Neural network system for determining optimal solution
US5349646A (en) * 1991-01-25 1994-09-20 Ricoh Company, Ltd. Signal processing apparatus having at least one neural network

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5285523A (en) * 1990-09-25 1994-02-08 Nissan Motor Co., Ltd. Apparatus for recognizing driving environment of vehicle
US5303328A (en) * 1990-10-24 1994-04-12 Hitachi, Ltd. Neural network system for determining optimal solution
US5349646A (en) * 1991-01-25 1994-09-20 Ricoh Company, Ltd. Signal processing apparatus having at least one neural network

Cited By (25)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6678669B2 (en) 1996-02-09 2004-01-13 Adeza Biomedical Corporation Method for selecting medical and biochemical diagnostic tests using neural network-related applications
WO1999009507A1 (fr) * 1997-08-14 1999-02-25 Adeza Biomedical Corporation Procedes permettant de selectionner, developper et ameliorer des tests diagnostiques d'etats lies a la grossesse
US6556977B1 (en) 1997-08-14 2003-04-29 Adeza Biomedical Corporation Methods for selecting, developing and improving diagnostic tests for pregnancy-related conditions
US7228295B2 (en) 1997-08-14 2007-06-05 Adeza Biomedical Corporation Methods for selecting, developing and improving diagnostic tests for pregnancy-related conditions
WO1999035602A1 (fr) * 1998-01-05 1999-07-15 Biosite Diagnostics, Inc. Methodes de controle de dosages
US6194222B1 (en) 1998-01-05 2001-02-27 Biosite Diagnostics, Inc. Methods for monitoring the status of assays and immunoassays
EP0962186A1 (fr) * 1998-06-02 1999-12-08 Bayer Corporation Système de diagnostic automatisé pour mettre en oeuvre des dosages immunologiques et des dosages de chimie-cliniques selon un algorithme réflexe
US7713703B1 (en) 2000-11-13 2010-05-11 Biosite, Inc. Methods for monitoring the status of assays and immunoassays
US9740817B1 (en) 2002-10-18 2017-08-22 Dennis Sunga Fernandez Apparatus for biological sensing and alerting of pharmaco-genomic mutation
US9582637B1 (en) 2002-10-18 2017-02-28 Dennis Sunga Fernandez Pharmaco-genomic mutation labeling
US9454639B1 (en) 2002-10-18 2016-09-27 Dennis Fernandez Pharmaco-genomic mutation labeling
US9384323B1 (en) 2002-10-18 2016-07-05 Dennis S. Fernandez Pharmaco-genomic mutation labeling
US8364413B2 (en) 2003-08-22 2013-01-29 Fernandez Dennis S Integrated biosensor and simulation system for diagnosis and therapy
US8370078B2 (en) 2003-08-22 2013-02-05 Fernandez Dennis S Integrated biosensor and simulation system for diagnosis and therapy
US8370070B2 (en) 2003-08-22 2013-02-05 Fernandez Dennis S Integrated biosensor and simulation system for diagnosis and therapy
US8374796B2 (en) 2003-08-22 2013-02-12 Dennis S. Fernandez Integrated biosensor and simulation system for diagnosis and therapy
US8423298B2 (en) 2003-08-22 2013-04-16 Dennis S. Fernandez Integrated biosensor and simulation system for diagnosis and therapy
US9110836B1 (en) 2003-08-22 2015-08-18 Dennis Sunga Fernandez Integrated biosensor and simulation system for diagnosis and therapy
US9111026B1 (en) 2003-08-22 2015-08-18 Dennis Sunga Fernandez Integrated biosensor and simulation system for diagnosis and therapy
US8346482B2 (en) 2003-08-22 2013-01-01 Fernandez Dennis S Integrated biosensor and simulation system for diagnosis and therapy
GB2405203B (en) * 2003-08-22 2008-01-16 Dennis Sunga Fernandez Biosensor with electronically configurable switching array
US9719147B1 (en) 2003-08-22 2017-08-01 Dennis Sunga Fernandez Integrated biosensor and simulation systems for diagnosis and therapy
GB2405203A (en) * 2003-08-22 2005-02-23 Dennis Sunga Fernandez Biosensor and simulation system
US10878936B2 (en) 2003-08-22 2020-12-29 Dennis Sunga Fernandez Integrated biosensor and simulation system for diagnosis and therapy
GB2429544A (en) * 2005-08-22 2007-02-28 Mv Res Ltd A classification system for recognising mis-labelled reference images

Similar Documents

Publication Publication Date Title
Hart et al. Evaluating black-boxes as medical decision aids: issues arising from a study of neural networks
US5839438A (en) Computer-based neural network system and method for medical diagnosis and interpretation
Fagerland et al. Multinomial goodness‐of‐fit tests for logistic regression models
WO2020004369A1 (fr) Dispositif de diagnostic d'électrocardiogramme basé sur un apprentissage automatique utilisant des images d'électrocardiogramme
US20040199482A1 (en) Systems and methods for automatic and incremental learning of patient states from biomedical signals
WO1998010697A9 (fr) Systeme de reseau neuronal informatise et procede de diagnostic medical et d'interpretation
WO1999027466A2 (fr) Systeme et procede permettant de realiser un controle de la qualite intelligent d'un processus
US11587679B2 (en) Generating computer models from implicitly relevant feature sets
Steen Approaches to predictive modeling
US11579155B2 (en) Using patient risk in analysis of quality control strategy for lab results
CN112435753A (zh) 医疗数据的自动验证
JP2004527825A (ja) バイオチップ測定装置で得たデータを評価するためのネットワーク
CN115691722A (zh) 医疗数据检测的质控方法、装置、设备、介质及程序产品
CN111382029B (zh) 基于pca和多维监测数据的主板异常诊断方法及装置
WO1997009678A1 (fr) Systeme de diagnostic d'organes biologiques par utilisation d'un reseau neuronal reconnaissant une erreur aleatoire d'entree
Leen et al. Statistical error detection for clinical laboratory tests
Bergman et al. The art and science of medical decision making
Möllenhoff et al. Testing similarity of parametric competing risks models for identifying potentially similar pathways in healthcare
JP2003050631A (ja) 異常診断システムの学習データ生成方法、異常診断システムの構築プログラム、異常診断プログラム、異常診断システムの構築装置および異常診断システム
CN116702521B (zh) 一种自动驾驶场景一致性对比方法、装置及电子设备
Sellbom et al. Introduction to exploratory factor analysis: An applied approach
CN117949539B (zh) 一种工程建筑强度检测系统及其检测方法
D’Silva et al. Prediction of Hypertension using Machine Learning
EP4288967A1 (fr) Procédé et appareil informatique pour évaluation de qualité de soins de santé
JP2000099130A (ja) プラント状態総合診断装置

Legal Events

Date Code Title Description
AK Designated states

Kind code of ref document: A1

Designated state(s): CA CN FI JP MX NO RU

AL Designated countries for regional patents

Kind code of ref document: A1

Designated state(s): AT BE CH DE DK ES FI FR GB GR IE IT LU MC NL PT SE

121 Ep: the epo has been informed by wipo that ep was designated in this application
DFPE Request for preliminary examination filed prior to expiration of 19th month from priority date (pct application filed before 20040101)
122 Ep: pct application non-entry in european phase
NENP Non-entry into the national phase

Ref country code: CA