WO2018168915A1 - Dispositif de détermination de score d'examen médical relatif à une fonction biologique et programme - Google Patents

Dispositif de détermination de score d'examen médical relatif à une fonction biologique et programme Download PDF

Info

Publication number
WO2018168915A1
WO2018168915A1 PCT/JP2018/009933 JP2018009933W WO2018168915A1 WO 2018168915 A1 WO2018168915 A1 WO 2018168915A1 JP 2018009933 W JP2018009933 W JP 2018009933W WO 2018168915 A1 WO2018168915 A1 WO 2018168915A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
determination
score
weight
subject
Prior art date
Application number
PCT/JP2018/009933
Other languages
English (en)
Japanese (ja)
Inventor
薫 酒谷
勝徳 大山
Original Assignee
学校法人日本大学
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 学校法人日本大学 filed Critical 学校法人日本大学
Publication of WO2018168915A1 publication Critical patent/WO2018168915A1/fr

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B10/00—Other methods or instruments for diagnosis, e.g. instruments for taking a cell sample, for biopsy, for vaccination diagnosis; Sex determination; Ovulation-period determination; Throat striking implements
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/026—Measuring blood flow
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters

Definitions

  • the present invention relates to a medical examination score determination apparatus and program for biological functions.
  • This application claims priority based on Japanese Patent Application No. 2017-048625 filed in Japan on March 14, 2017, the contents of which are incorporated herein by reference.
  • MMSE Mini Mental State Examination
  • a subject is asked a question, and the answer is scored to determine whether or not the subject has dementia and its degree.
  • a diagnostic device such as an electroencephalogram measuring device, a nuclear magnetic resonance imaging device, or a CT scan device. It is disclosed (see, for example, Patent Document 1).
  • the present invention has been made in view of the above points, and provides a medical examination score determination apparatus and program for a biological function capable of easily performing a medical examination determination based on self-reporting.
  • the present invention has been made to solve the above-mentioned problems, and one aspect of the present invention includes a medical examination determination score based on self-report among data relating to a first subject, A weight calculating unit that calculates a weight based on first data including at least one of data representing an examination result of the examination and data representing physical characteristics; the weight calculated by the weight calculating unit; The determination score of the second subject is determined based on second data including at least one of data representing a medical examination result and data representing physical characteristics among data relating to two subjects. And a score determination unit for medical examination of a biological function.
  • the determination score is a score of a test for dementia based on self-report.
  • the weight calculation unit calculates the weight by machine learning based on the first data.
  • the data indicating the test result of the medical test is the result of the general blood test of the first subject.
  • the data includes at least blood test information to be shown.
  • the data representing the examination result of the medical examination is the first subject by the noninvasive brain activity measurement apparatus.
  • This data includes at least the measurement result of the brain activity.
  • the computer includes data on the first test subject including the determination score of the medical examination based on the self-report, and the data representing the examination result of the medical examination, and the physical characteristics
  • a weight calculation step for calculating a weight based on first data including at least one of the data representing the data, the weight calculated by the weight calculation step, and a medical examination of the data related to the second subject
  • a program for executing a score determination step of determining the determination score of the second subject based on data representing a result and second data including at least one of data representing physical characteristics .
  • FIG. 1 is a diagram illustrating an example of a functional configuration of a determination system 1 according to the present embodiment.
  • the determination system 1 is based on medical data based on self-report on the subject's biological function based on the body data PD representing the physical characteristics of the subject and the examination result data MT representing the examination result of the medical examination on the biological function. Determine the test score.
  • the medical examination based on the self-report on the biological function is, for example, a so-called “mini mental state examination” (MMSE) that is generally used for determination of dementia.
  • MMSE mini mental state examination
  • the mini-mental state test consists of eleven questions with a maximum score of 30 that cover orientation, memory, computation, linguistic ability, graphic ability, etc.
  • the score of the test subject's answer to the question of the mini mental state test is determined to be normal when the score is 24 or higher, moderately degraded when the score is less than 20, and severely degraded when the score is less than 10.
  • the mini mental state examination is an examination in which an examiner (such as a doctor) and a subject face each other and repeatedly ask and answer questions. Questions and answers in the mini-mental state examination take about 5 to 10 minutes.
  • the determination system 1 refers to the physical data PD representing the physical characteristics of the subject and the examination result data MT representing the examination result of the medical examination, so that the mini-mental can be performed without performing the tasks such as questioning and answering in person.
  • a determination score corresponding to the state inspection score is determined. Therefore, the determination system 1 can reduce labor and time due to the questions and answers being performed face-to-face.
  • a functional configuration of the determination system 1 that realizes determination of a determination score without performing tasks such as questions and answers by face-to-face will be described with reference to FIG.
  • the determination system 1 includes a teacher data supply unit 10, a determination device 20, a determination data supply unit 30, and a presentation unit 40.
  • the determination device 20 calculates the weight W by a machine learning technique called deep learning. Deep learning is a machine learning technique using a multilayer neural network (a neural network having two or more hidden layers).
  • the determination device 20 calculates the weight W based on the teacher data supplied from the teacher data supply unit 10.
  • the determination device 20 determines a determination score based on the determination data supplied from the determination data supply unit 30 and the calculated weight W.
  • the teacher data supply unit 10 supplies teacher data to the determination device 20.
  • the teacher data is data in which a teacher determination score LSC, teacher test result data LMT, and teacher body data LPD are associated with each other.
  • the output (teacher determination score LSC) with respect to the inputs (teacher test result data LMT and teacher body data LPD) is known in advance. Therefore, the determination apparatus 20 can use the teacher data to calculate a weight W that outputs a correct result with respect to the input.
  • the user of the determination system 1 needs to collect a predetermined number or more of data used as teacher data in advance.
  • the determination data supply unit 30 supplies determination data to the determination device 20.
  • the determination data is data composed of a set of test result data MT and body data PD.
  • the type of data included in the teacher test result data LMT corresponds to the type of data included in the test result data MT.
  • the type of data included in the teacher physical data LPD corresponds to the type of data included in the physical data PD.
  • the test result data MT and the body data PD may be collectively referred to as explanatory variables.
  • the teacher data supply unit 10 and the determination data supply unit 30 may be human interface devices such as a keyboard, a tablet, and a scanner, or may be information storage devices such as a server.
  • the teacher determination score LSC is a score of the answer of the subject of MMSE.
  • the test result data MT is information indicating a test result of a medical test of the subject.
  • the test result data MT includes, for example, blood test information GBT and brain activity measurement result information BA.
  • the blood test information GBT is information indicating the test result of the subject's general blood test.
  • the general blood test is a blood test in which test items are general, for example, a blood test performed in a general health checkup. An example of the items of the general blood test will be described with reference to FIG.
  • the brain activity measurement result information BA is the result of the measurement result of the brain activity of the subject by the noninvasive brain activity measurement device.
  • the body data PD is, for example, age information AG that is data indicating the age of the subject.
  • FIG. 2 is a diagram illustrating an example of blood test information GBT included in the test result data MT supplied by the determination data supply unit 30 of the present embodiment.
  • Blood test information GBT includes test items for general blood tests and test values for each test item.
  • blood test information GBT includes white blood cell count (WBC), MCV, MCH, platelet count (PLT), total protein (TP), albumin (Alb), uric acid (UA), urea nitrogen (BUN), creatinine. (Crea), sodium (So), and crawl (Cl) are included as inspection items.
  • WBC white blood cell count
  • MCV platelet count
  • TP total protein
  • albumin Alb
  • uric acid U
  • BUN urea nitrogen
  • Crea creatinine.
  • So crawl
  • crawl crawl
  • the blood test information GBT has a white blood cell count (WBC) of a reference value of 4000 to 9000 [pieces / ⁇ L], a test value of 5000 [pieces / ⁇ L], and an MCV of Information of inspection value 90 [fl]... Is included for reference values 84 to 99 [fl].
  • WBC white blood cell count
  • a non-invasive brain activity measuring device measures the brain activity of a subject by measuring the blood flow of the brain using a non-invasive detection means such as a near infrared ray.
  • This non-invasive brain activity measuring apparatus measures, for example, measurement target items such as hemoglobin concentration (Hb), oxygenated hemoglobin concentration (HbO2), deoxygenated hemoglobin concentration (HbDO2), and oxygen saturation (SO2).
  • Hb hemoglobin concentration
  • HbO2 oxygenated hemoglobin concentration
  • HbDO2 deoxygenated hemoglobin concentration
  • SO2 oxygen saturation
  • the determination data supply unit 30 includes the left brain side hemoglobin concentration L_Hb, the right brain side hemoglobin concentration R_Hb, the left brain side oxygenated hemoglobin concentration L_HbO2, the right brain side oxygenated hemoglobin concentration R_HbO2, the left brain side deoxygenated hemoglobin concentration L_HbDO2, and the right brain.
  • the side deoxygenated hemoglobin concentration R_HbDO2, the left brain side oxygen saturation L_SO2, and the right brain side oxygen saturation R_SO2 are supplied to the determination device 20 as a part of the test result data MT.
  • the determination apparatus 20 includes a teacher determination score acquisition unit 210, a teacher examination result data acquisition unit 220, a teacher body data acquisition unit 230, a weight calculation unit 240, an examination result acquisition unit 250, a body data acquisition unit 260, A score determination unit 270.
  • the teacher determination score acquisition unit 210 acquires the teacher determination score LSC supplied by the teacher data supply unit 10.
  • the teacher determination score acquisition unit 210 supplies the acquired teacher determination score LSC to the weight calculation unit 240.
  • the teacher examination result data acquisition unit 220 acquires the teacher examination result data LMT supplied by the teacher data supply unit 10.
  • the teacher examination result data acquisition unit 220 supplies the acquired teacher examination result data LMT to the weight calculation unit 240.
  • the teacher body data acquisition unit 230 acquires the teacher body data LPD supplied from the teacher data supply unit 10.
  • the teacher body data acquisition unit 230 supplies the acquired teacher body data LPD to the weight calculation unit 240.
  • the weight calculation unit 240 includes a teacher determination score LSC acquired by the teacher determination score acquisition unit 210, teacher test result data LMT acquired by the teacher test result data acquisition unit 220, and teacher body data acquired by the teacher body data acquisition unit 230.
  • the weight W is calculated based on the LPD.
  • the weight calculation unit 240 outputs the calculated weight W to the score determination unit 270.
  • the inspection result acquisition unit 250 acquires inspection result data MT supplied from the determination data supply unit 30.
  • the inspection result acquisition unit 250 supplies the acquired inspection result data MT to the score determination unit 270.
  • the body data acquisition unit 260 acquires the body data PD supplied from the determination data supply unit 30.
  • the body data acquisition unit 260 supplies the acquired body data PD to the score determination unit 270.
  • the score determination unit 270 determines a determination score based on the test result data MT acquired by the test result acquisition unit 250, the body data PD acquired by the body data acquisition unit 260, and the weight W calculated by the weight calculation unit 240. .
  • the score determination unit 270 outputs the determination result SC of the determination score to the presentation unit 40.
  • the presentation unit 40 is, for example, a display or a printer, and presents the determination result SC supplied from the score determination unit 270 of the determination device 20 by a display unit such as display or printing.
  • the presentation unit 40 may be a storage device such as a network server. In this case, the presentation unit 40 stores the determination result SC supplied from the score determination unit 270, and supplies the stored determination result SC to another device.
  • FIG. 3 is a diagram illustrating an example of a learning process of the determination device 20 according to the present embodiment.
  • the teacher determination score acquisition unit 210 acquires the teacher determination score LSC from the teacher data supply unit 10.
  • the teacher examination result data acquisition unit 220 acquires the teacher examination result data LMT from the teacher data supply unit 10.
  • the teacher body data acquisition unit 230 acquires the teacher body data LPD from the teacher data supply unit 10.
  • the weight calculation unit 240 calculates the weight W based on the teacher determination score LSC acquired in Step S10, the teacher examination result data LMT acquired in Step S20, and the teacher body data LPD acquired in Step S30. To do. In deep learning, the degree of coupling between units in the network and the bias term are adjusted so that the desired prediction can be obtained.
  • the inter-unit coupling degree is a weight W.
  • the inter-unit coupling degree and the bias term may be collectively referred to as a weight W.
  • calculating the weight W may be referred to as learning.
  • the explanatory variables in the present embodiment are data included in the test result data MT and the body data PD, respectively.
  • One advantage of using deep learning in the present embodiment is that the weight W is automatically selected by learning even if the explanatory variables increase.
  • FIG. 4 is a diagram illustrating an example of the neural network NN of the present embodiment.
  • the neural network NN includes an input layer IL, hidden layers HL1, HL2, HL3, and HL4, and an output layer OL.
  • the input layer IL, the hidden layers HL1, HL2, HL3, and HL4, and the output layer OL are each composed of units arranged in a line.
  • the neural network NN is a neural network called a feedforward network.
  • a feedforward network is a neural network in which information propagates in one direction from an input layer to an output layer. In this embodiment, a feedforward network in which the number of hidden layers is four and the number of units in each hidden layer is two is used.
  • the hidden layer HL1 includes a unit H1-1 and a unit H1-2.
  • the hidden layer HL2 includes a unit H2-1 and a unit H2-2.
  • the hidden layer HL3 includes a unit H3-1 and a unit H3-2.
  • the hidden layer HL4 includes a unit H4-1 and a unit H4-2.
  • the output layer OL is composed of a unit O-1 and a unit O-2.
  • the number of units in the output layer OL being 2 corresponds to the determination device 20 performing determination for classifying the subject into two classes.
  • the two classes are composed of a class having an MMSE answer score of 24 points or more (normal) and a class having a score of less than 24 points.
  • the units constituting the hidden layers HL1, HL2, HL3, and HL4 and the output layer OL are coupled to the outputs from a plurality of units constituting the layer on the input layer IL side adjacent to the respective layers constituting the unit. Multiply by degree and add bias term as input.
  • the inter-unit coupling degree (weight W) and the bias term are different for each set of the output unit and the input unit.
  • FIG. 5 is a diagram illustrating an example of a determination process of the determination device 20 according to the present embodiment.
  • Step S50 The inspection result acquisition unit 250 acquires the inspection result data MT from the determination data supply unit 30.
  • Step S60 The body data acquisition unit 260 acquires the body data PD from the determination data supply unit 30.
  • Step S70 The score determination unit 270 determines a determination score based on the test result data MT acquired in step S50, the body data PD acquired in step S60, and the weight W calculated in step S40 of FIG.
  • the determination result SC indicates whether the score of the subject's MMSE answer is 24 points or more, or the score of the subject's MMSE answer is less than 24 points.
  • Step S80 The score determination unit 270 outputs the determination result SC to the presentation unit 40 and ends the series of processes.
  • the determination accuracy of the determination device 20 will be described.
  • a sufficient number of determination data may not be available. Therefore, a part of the teacher data is extracted and used as determination data, and the remaining data is used for calculating the weight W to verify the determination accuracy.
  • a technique called Leave-One-Out cross validation is used.
  • the number of teacher data is N (N is a natural number).
  • the determination device 20 extracts one piece of data from the teacher data and uses it as determination data, and calculates the weight W using the remaining N ⁇ 1 pieces of data.
  • the determination device 20 determines a determination score based on the determination data and the calculated weight W.
  • the determination device 20 determines whether the determination result SC is correct by comparing the determination result SC of the determination score with the teacher determination score LSC corresponding to the teacher data used as the determination data.
  • the determination apparatus 20 repeats this process N times until all of the teacher data is used as determination data.
  • the test results of 98 subjects over 40 years old, the results of the general blood test, the measurement results of the subject's brain activity by the non-invasive brain activity measuring device, the age, and the MMSE answer score are used as teacher data.
  • the determination device 20 outputs a correct determination result SC for 96 pieces of determination data. That is, in the above case, it is verified that the determination accuracy of the determination device 20 is 98%.
  • the determination device 20 is based on the weight W calculated by the weight calculation unit 240 and the determination data including the examination result data MT and the body data PD among the data related to the subject.
  • the determination score of the subject is determined.
  • the determination device 20 calculates the weight W based on the teacher determination score LSC, the teacher examination result data LMT, and the teacher body data LPD among the data regarding the subject. Therefore, according to the determination apparatus 20 of the present embodiment, it is possible to reduce the labor and time that have conventionally occurred due to the questions and answers being performed face-to-face in a medical examination based on self-reporting.
  • FIG. 6 is a diagram illustrating an example of a procedure for calculating the weight W according to the present embodiment.
  • the inter-unit coupling degree and the bias term are collectively referred to as a weight W.
  • the weight calculator 240 performs learning of the neural network NN for each hidden layer as an example.
  • the neural network NN includes an input layer, a first hidden layer, and an output layer.
  • the weight calculation unit 240 calculates the weight W for the first hidden layer.
  • the weight calculation unit 240 adds a second hidden layer between the first hidden layer and the output layer to the neural network NN.
  • the weight calculation unit 240 calculates the weight W for the second hidden layer.
  • the weight calculation unit 240 calculates the weight W for each hidden layer by repeating the above procedure.
  • the weight calculation unit 240 ends the calculation of the weight W when the calculation of the weight W for the fourth hidden layer is completed.
  • the weight calculation unit 240 may perform learning of the neural network NN for all hidden layers.
  • this calculation procedure can be executed by a computer.
  • the weight calculation unit 240 initializes the weight W and the bias B.
  • an element of weight W is an element w ji
  • an element of bias B is an element b ji .
  • the element w ji and the element b ji are used when calculating an input to a unit constituting the (j + 1) th layer adjacent to the jth layer.
  • the weight calculation unit 240 sets a random number distributed between 0 and 1 generated from the normal distribution as the value of w ji and sets 0 as the value of b ji .
  • the weight calculation unit 240 rearranges the N teacher data at random.
  • Step S401 The weight calculation unit 240 extracts B unused pieces (B is a natural number) in ascending order from the randomly rearranged teacher data. However, when there is no unused teacher data, the weight calculation unit 240 rearranges the teacher data at random again to obtain unused teacher data.
  • the extracted B teacher data is called a mini batch.
  • Step S402 The weight calculation unit 240 applies a dropout method to the neural network NN.
  • Dropout is a technique for making overlearning less likely to occur.
  • the weight calculation unit 240 invalidates the units constituting each layer with a certain probability p (p is a real number from 0 to 1).
  • invalidating a unit means that the output from the unit is temporarily treated as zero.
  • the value of p is set to 0.5, but the value of p is not limited to this.
  • the probability p may be different for each layer.
  • the score determination unit 270 multiplies the output of each of the input layer and the hidden layer of the neural network NN according to the invalidation probability p.
  • the weight calculation unit 240 extracts one teacher data from the extracted mini-batch.
  • x i is a vector having components of the number of types of data included in the teacher examination result data LMT and the teacher body data LPD.
  • the Tanh function is used as the activation function.
  • the activation function may be a function other than the Tanh function.
  • the activation function may be, for example, a logistic function or a normalized linear function.
  • the weight calculation unit 240 calculates the output from each unit of the second hidden layer by substituting the input into the activation function.
  • the weight calculation unit 240 calculates an objective function.
  • the objective function is a function of the weight W, and a smaller value is given as the matching degree between the output of the neural network NN and the teacher determination score LSC included in the teacher data is higher.
  • the objective function is given by equation (1).
  • the number N of teacher data in Expression (1) is equal to the number B of teacher data included in the mini-batch.
  • the objective function is an average of the loss function l given for each teacher data.
  • y i is a two-component vector.
  • y i (1, 0) when the teacher determination score LSC is 24 points or more.
  • y i (0, 1) is given.
  • the loss function l uses a square error as an example, but is not limited thereto.
  • the loss function l may be, for example, an entropy error.
  • the weight calculation unit 240 updates the weight W using the gradient descent method.
  • the gradient descent method is a method of calculating a parameter so that the parameter is changed in a gradient direction in which an error (objective function) is small.
  • Performing learning using only a part of the teacher data by using a mini-batch as in the present embodiment is called a stochastic gradient descent (SGD).
  • SGD stochastic gradient descent
  • the weight calculation unit 240 updates the weight W according to Expression (2).
  • Step S406 The weight calculation unit 240 determines whether or not the objective function has converged by updating the weight W in step S405. If the weight calculation unit 240 determines that the objective function has converged (YES), the weight W calculation process ends. On the other hand, if it is determined that the objective function has not converged (NO), the process of step S401 is performed.
  • One of the reasons for learning using the stochastic gradient descent method is to prevent the weight W from falling into a local solution before the optimal solution where the objective function converges.
  • the explanatory variables are age information AG, blood test information GBT, and brain activity measurement result information BA, but the explanatory variables are not limited thereto.
  • the explanatory variables are not limited thereto.
  • only some of age information AG, blood test information GBT, and brain activity measurement result information BA may be used as explanatory variables.
  • the explanatory variable only a part of the test values for each test item included in the blood test information GBT may be used, or a part of the measurement values of the measurement target item included in the brain activity measurement result information BA. You may use only.
  • age information AG albumin test value, uric acid test value, left brain side hemoglobin concentration L_Hb, right brain side hemoglobin concentration R_Hb, left brain side oxygen saturation L_SO2, and right brain side oxygen saturation Only R_SO2 may be used.
  • age information AG left brain side hemoglobin concentration L_Hb, right brain side hemoglobin concentration R_Hb, left brain side oxygen saturation L_SO2, and right brain side oxygen saturation R_SO2 may be used as explanatory variables.
  • age information AG albumin test value, uric acid test value
  • left brain side hemoglobin concentration L_Hb left brain side hemoglobin concentration R_Hb
  • left brain side oxygen saturation L_SO2 left brain side oxygen saturation L_SO2
  • right brain side oxygen saturation R_SO2 may be used as explanatory variables.
  • other information indicating the physical characteristics of the subject may be added to the body data PD, or other information indicating the physical characteristics of the subject may be used instead of the age information AG.
  • Other information indicating the physical characteristics of the subject may be, for example, information indicating sex, information indicating height, information indicating weight, information indicating obesity, and the like.
  • the information indicating the degree of obesity is, for example, a body mass index (BMI: Body Mass Index).
  • other information indicating the test result of the medical test of the subject other than the blood test information GBT and the brain activity measurement result information BA may be added to the test result data MT, or the blood test information Instead of the GBT and the brain activity measurement result information BA, other information indicating the test result of the medical examination of the subject may be used.
  • MMSE has been described as an example of a medical examination based on self-reporting.
  • the medical examination based on self-reporting may be other than MMSE.
  • the medical examination based on self-report may be, for example, a medical examination in which results are scored, such as a state-trait anxiety examination (STAI) or a personality examination.
  • STAI state-trait anxiety examination
  • the number of hidden layers of the neural network NN is four and the number of units of each hidden layer is two.
  • the number of hidden layers and the number of units of each hidden layer are described here. Not exclusively.
  • the case where the number of units in the output layer of the neural network NN is 2 has been described.
  • the number of units in the output layer is not limited to this.
  • the determination device 20 may have one unit in the output layer. In that case, the determination device 20 determines the determination score according to one value of the output of the output layer. Further, the determination device 20 may classify the determination score more finely by setting the number of units of the output layer of the neural network NN to 3 or more.
  • the determination apparatus 20 based on the weight W calculated by the weight calculation unit 240 and the determination data including the test result data MT and the body data PD among the data related to the subject, Determine the test score of the subject.
  • the determination device 20 calculates the weight W based on the teacher determination score LSC, the teacher examination result data LMT, and the teacher body data LPD among the data regarding the subject. Therefore, according to the determination apparatus 20, the medical examination based on the self-report can be easily determined.
  • the determination score is the same as the score of the dementia test (MMSE) based on self-report. For this reason, according to the determination apparatus 20, the determinant who has determined the dementia state of the subject using the conventional MMSE can easily understand the dementia determination score.
  • the weight W is calculated by machine learning. For this reason, according to the determination apparatus 20, the inspector is not subject to the preconception about the meaning of the physical data representing the physical characteristics of the subject or the examination result data representing the examination result of the medical examination.
  • the determination of the medical examination that has been performed based on the self-report of the subject can be easily performed without being based on the self-report of the subject.
  • the data indicating the test result of the medical test includes the blood test information GBT indicating the result of the general blood test of the subject.
  • the determination apparatus 20 a special test
  • the data representing the test result of the medical examination includes the brain activity measurement result information BA indicating the measurement result of the brain activity of the subject by the noninvasive brain activity measurement device. For this reason, according to the determination apparatus 20, a special test
  • each part with which each apparatus in said embodiment is provided may be implement
  • Each unit included in each device is configured by a memory and a CPU (central processing unit), and a program for realizing the function of each unit included in each device is loaded into the memory and executed to realize the function. It may be.
  • a program for realizing the function of each unit included in each device is recorded on a computer-readable recording medium, and the program recorded on the recording medium is read into a computer system and executed, whereby the control unit You may perform the process by each part with which it is equipped.
  • the “computer system” includes an OS and hardware such as peripheral devices.
  • the “computer system” includes a homepage providing environment (or display environment) if a WWW system is used.
  • the “computer-readable recording medium” refers to a storage device such as a flexible medium, a magneto-optical disk, a portable medium such as a ROM or a CD-ROM, and a hard disk incorporated in a computer system.
  • the “computer-readable recording medium” dynamically holds a program for a short time like a communication line when transmitting a program via a network such as the Internet or a communication line such as a telephone line.
  • a volatile memory in a computer system serving as a server or a client in that case, and a program that holds a program for a certain period of time are also included.
  • the program may be a program for realizing a part of the functions described above, and may be a program capable of realizing the functions described above in combination with a program already recorded in a computer system.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Public Health (AREA)
  • Surgery (AREA)
  • Veterinary Medicine (AREA)
  • Pathology (AREA)
  • Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Animal Behavior & Ethology (AREA)
  • Biophysics (AREA)
  • Optics & Photonics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Hematology (AREA)
  • Cardiology (AREA)
  • Physiology (AREA)
  • Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
  • Medical Treatment And Welfare Office Work (AREA)

Abstract

L'invention concerne un dispositif de détermination de score d'un examen médical relatif à une fonction biologique qui comprend : une unité de calcul de poids qui calcule un poids sur la base de premières données comprenant, parmi des données concernant un premier sujet, au moins l'une parmi des données qui comprennent un score de détermination d'un examen médical sur la base du propre rapport du sujet et indiquent un résultat d'examen de l'examen médical, et des données qui indiquent une caractéristique physique ; et une unité de détermination de score qui détermine un score de détermination d'un second sujet sur la base du poids calculé par l'unité de calcul de poids, et des secondes données comprenant, parmi des données concernant le second sujet, au moins l'une parmi des données qui indiquent un résultat d'examen d'un examen médical et des données qui indiquent une caractéristique physique.
PCT/JP2018/009933 2017-03-14 2018-03-14 Dispositif de détermination de score d'examen médical relatif à une fonction biologique et programme WO2018168915A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2017-048625 2017-03-14
JP2017048625A JP6845716B2 (ja) 2017-03-14 2017-03-14 生体機能についての医学的検査の得点判定装置、及びプログラム

Publications (1)

Publication Number Publication Date
WO2018168915A1 true WO2018168915A1 (fr) 2018-09-20

Family

ID=63522283

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2018/009933 WO2018168915A1 (fr) 2017-03-14 2018-03-14 Dispositif de détermination de score d'examen médical relatif à une fonction biologique et programme

Country Status (2)

Country Link
JP (1) JP6845716B2 (fr)
WO (1) WO2018168915A1 (fr)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115190778A (zh) * 2020-03-19 2022-10-14 欧姆龙健康医疗事业株式会社 生物体信息获取装置及生物体信息获取方法
JPWO2022075461A1 (fr) 2020-10-09 2022-04-14

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2012502279A (ja) * 2008-09-04 2012-01-26 レドックス−リアクティブ リエイジェンツ リミテッド ライアビリティー カンパニー アルツハイマー病の診断、モニタリングおよび／または病期診断のための、バイオマーカー、キットおよび方法
WO2012165602A1 (fr) * 2011-05-31 2012-12-06 国立大学法人名古屋工業大学 Equipement de détermination de dysfonctionnement cognitif, système de détermination de dysfonctionnement cognitif et programme
US20140226882A1 (en) * 2011-09-16 2014-08-14 Mcgill University Simultaneous segmentation and grading of structures for state determination
WO2015037089A1 (fr) * 2013-09-11 2015-03-19 日立コンシューマエレクトロニクス株式会社 Procédé d'évaluation de dysfonction cérébrale, dispositif d'évaluation de dysfonction cérébrale et programme de ceux-ci

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4876207B2 (ja) * 2010-06-11 2012-02-15 国立大学法人 名古屋工業大学 認知機能障害危険度算出装置、認知機能障害危険度算出システム、及びプログラム
CN103392183B (zh) * 2010-12-20 2017-05-10 皇家飞利浦电子股份有限公司 用于识别处于转化为阿尔茨海默病的风险中的具有轻度认知障碍的患者的系统
US9474481B2 (en) * 2013-10-22 2016-10-25 Mindstrong, LLC Method and system for assessment of cognitive function based on electronic device usage
JP6702836B2 (ja) * 2016-09-28 2020-06-03 ハルメク・ベンチャーズ株式会社 認知症判定得点算出装置及びそのプログラム

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2012502279A (ja) * 2008-09-04 2012-01-26 レドックス−リアクティブ リエイジェンツ リミテッド ライアビリティー カンパニー アルツハイマー病の診断、モニタリングおよび／または病期診断のための、バイオマーカー、キットおよび方法
WO2012165602A1 (fr) * 2011-05-31 2012-12-06 国立大学法人名古屋工業大学 Equipement de détermination de dysfonctionnement cognitif, système de détermination de dysfonctionnement cognitif et programme
US20140226882A1 (en) * 2011-09-16 2014-08-14 Mcgill University Simultaneous segmentation and grading of structures for state determination
WO2015037089A1 (fr) * 2013-09-11 2015-03-19 日立コンシューマエレクトロニクス株式会社 Procédé d'évaluation de dysfonction cérébrale, dispositif d'évaluation de dysfonction cérébrale et programme de ceux-ci

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
LIU, SIQI ET AL.: "Early diagnosis of Alzheimer's disease with deep learning", 2014 IEEE 11TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI), 31 July 2014 (2014-07-31), pages 1015 - 1018, XP032779241, ISBN: 978-1-4673-1961-4 *

Also Published As

Publication number Publication date
JP6845716B2 (ja) 2021-03-24
JP2018149168A (ja) 2018-09-27

Similar Documents

Publication Publication Date Title
Heilman et al. Conceptual apraxia from lateralized lesions
Crane et al. Routine collection of patient-reported outcomes in an HIV clinic setting: the first 100 patients
Schuman et al. Quantification of nerve fiber layer thickness in normal and glaucomatous eyes using optical coherence tomography: a pilot study
Robertson et al. The Tourette syndrome diagnostic confidence index: development and clinical associations
Chiang et al. Interexpert agreement of plus disease diagnosis in retinopathy of prematurity
Kaplan et al. Validity of the Quality of Well-Being Scale for persons with human immunodeficiency virus infection
Hoffman et al. Virtual reality bringing a new reality to postthoracotomy lung cancer patients via a home-based exercise intervention targeting fatigue while undergoing adjuvant treatment
Merluzzi et al. Self‐efficacy for coping with cancer: revision of the Cancer Behavior Inventory (version 2.0)
Blackwood et al. On the problems of mixing RCTs with qualitative research: the case of the MRC framework for the evaluation of complex healthcare interventions
Albert et al. A prospective study of preferences and actual treatment choices in ALS
Bardin et al. Pattern classification of volitional functional magnetic resonance imaging responses in patients with severe brain injury
Mathis et al. Reliability and validity of the patient-specific functional scale in community-dwelling older adults
O'Neill et al. Do first impressions count? Frailty judged by initial clinical impression predicts medium‐term mortality in vascular surgical patients
Hepping et al. The influence of hand preference on grip strength in children and adolescents; a cross-sectional study of 2284 children and adolescents
Bergsma et al. Quality of life: does measurement help?
Sterling et al. The influence of preparedness, mutuality, and self-efficacy on home care workers' contribution to self-care in heart failure: a structural equation modeling analysis
WO2018168915A1 (fr) Dispositif de détermination de score d'examen médical relatif à une fonction biologique et programme
Filshtein et al. Differential item functioning of the everyday cognition (ECoG) scales in relation to racial/ethnic groups
Rowe The impact of internal and external resources on functional outcomes in chronic illness
Stickel et al. Hearing sensitivity, cardiovascular risk, and neurocognitive function: the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)
Silverman et al. FEV1performance among patients with acute asthma: results from a multicenter clinical trial
Lakshmi et al. Non-invasive estimation of haemoglobin level using pca and artificial neural networks
Sample et al. Pattern of early visual field loss in HIV-infected patients
JP6702836B2 (ja) 認知症判定得点算出装置及びそのプログラム
Aldalur et al. Psychometric properties of the SAFE-D: A measure of acculturative stress among deaf undergraduate students.

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18767794

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 18767794

Country of ref document: EP

Kind code of ref document: A1