EP1434516A2 - Dispositif d'evaluation de donnees brutes psychologiques et biomedicales - Google Patents

Dispositif d'evaluation de donnees brutes psychologiques et biomedicales

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Publication number
EP1434516A2
EP1434516A2 EP02778969A EP02778969A EP1434516A2 EP 1434516 A2 EP1434516 A2 EP 1434516A2 EP 02778969 A EP02778969 A EP 02778969A EP 02778969 A EP02778969 A EP 02778969A EP 1434516 A2 EP1434516 A2 EP 1434516A2
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EP
European Patent Office
Prior art keywords
data
neural network
input
psychological
subject
Prior art date
Legal status (The legal status 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 status listed.)
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Application number
EP02778969A
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German (de)
English (en)
Inventor
Gernot Schuhfried
Jörg A. PRIELER
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Dr G Schuhfried GmbH
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Dr G Schuhfried GmbH
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Publication date
Application filed by Dr G Schuhfried GmbH filed Critical Dr G Schuhfried GmbH
Publication of EP1434516A2 publication Critical patent/EP1434516A2/fr
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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

Definitions

  • the invention relates to the evaluation of psychological and / or biomedical raw data in the form of a profile vector which comprises data relating to a subject and obtained in the course of psychological / biomedical testing, which, however, at least in part originate from purely psychological tests.
  • tests have been used in which various psychological and / or biomedical parameters are recorded, for example by means of measurements, test situations or interviews, and an overall judgment is determined from them, e.g. regarding the suitability of the test subject (e.g. as a driver or aircraft pilot), psychological resilience, the presence of a mental disorder (e.g. depression). These tests are often carried out in the form of test batteries.
  • Cut-off scores are currently used primarily in traffic psychology, although falling below a prescribed cut-off score in a single measured value can lead to the driver's license being withdrawn.
  • the cut-off scores given in the individual test parameters are based on expert ratings. However, studies on the possibilities of compensating for deficits or increasing individual deficits are pending.
  • the correlation and regression analysis approaches can be further divided into linear and curve-linear methods.
  • the main representatives of linear statistical techniques, which are also used in most studies on the classification of subjects, are regression analysis and discriminatory analysis, cf. R.S. Jäger, "Diagnostic Judgment Formation” in Encyclopedia of Psychology, ed. K-J. Groffmann and L. Michel, Göttingen: Hogrefe 1982.
  • curvilinear models the basic idea of which is often referred to as “configurability”, assume that there is a non-linear relationship between the predictor variables (eg test data, information from anamnesis) and the criterion variable (statements about the judgment, eg “successful "versus” unsuccessful "). Exactly these curvilinear relationships mostly represent reality in the field of psychology, but such relationships are naturally difficult to model. In today's studies, therefore, the discriminant analysis is mainly used in the classification of people.
  • the principle of discriminant analysis with two qualitatively different forms of a criterion variable is to find a measure by a linear and weighted combination of the individual characteristics that optimally separates the two assignment groups. This means that the variance of the combined point values between the two groups is minimized compared to the variance within one group rr.
  • the calculation process is ended by determining a dislocation function and comparing the empirically ascertained assignments of the persons to the two assignment groups, which are determined on the basis of the discriminant function.
  • This discriminant function has the property that best separates given classes.
  • the ß weights calculated for the individual predictors are partial regression coefficients, which immediately illustrates the relationship of the discriminant analysis to the multiple regression.
  • the discriminant analysis was originally developed for assignments in the context of anthropology, cf.
  • the distributions of the characteristic values of the predictor variables are multivariate normal for each partial sample formed according to the criterion
  • DE 19831 109 AI proposes to evaluate EEG data from premature and newborn babies with the help of a neural network in order to recognize disturbances in respiratory regulation.
  • No. 5,724,987 relates to a method for regulating training units on the basis of neurophysiological derivations, in which physiological parameters are compressed into an index of attention and cognitive stress by means of a neural network and the training session is controlled with the aid of this index.
  • EP 0699413 AI deals with a similar method in which EEG signals are used to analyze the physiological and mental state of a driver.
  • physiological data differs significantly from the evaluation of psychological data which are subject to the invention.
  • physiological data can be measured, often even using standardized methods, so that tendencies from the measured material can be clearly proven and the resulting conclusions can often be justified.
  • Reproducibility is often not a problem.
  • the analysis of psychological data still relies on statistical methods, and the underlying data are considered to be less manageable and comparable;
  • the acquisition of psychological data is usually difficult to reproduce, and this data is therefore subject to corresponding uncertainties. This inevitably affects the reliability of the conclusions derived from it.
  • the results are not fed back to the evaluation.
  • the object of the invention is to show a way for objectifiable classifications for raw data of the type described at the outset, which have a high level of reliability.
  • the invention allows the statistical evaluation of the psychological / biomedical data through the use of non-linear automatons, namely neural networks. These are in different areas of technology, e.g. at the pattern recognition, known.
  • EP 1 022632 AI describes the use of a neural network for checking the functionality of an electrical device; the use of neural networks for the evaluation of physiological signals was presented above.
  • the invention now proposes neural networks for use in psychology. Compared to established evaluation methods, the evaluation according to the invention enables a significantly higher rate of reliability.
  • the neural network provided according to the invention must be trained before it is actually used.
  • the starting point for the learning process of the network is the previously determined initial and practical values of a so-called learning sample.
  • the network should be able to independently assign people to the learning sample.
  • the classification of individual observations can be carried out, which are entered into the network after the training.
  • the network generalizes its "experiences" made in relation to the training collection to the new individual case.
  • neural networks Compared to the known statistical methods of forming judgments, neural networks have the following favorable properties for classification tasks: 1. Neural networks are learnable and changeable, ie, they can "learn" the optimal assignment from existing data sets and, if desired, change successively with the inclusion of new (learning) data.
  • Neural networks can map nonlinear relationships.
  • Neural networks assume no requirements with regard to the data properties and the distribution of the predictor variables; the only requirement of the method concerns the completeness of the data sets in the learning sample.
  • the second characteristic, the simulation of non-linearities, is particularly important in psychology, since people rarely "react linearly" and moderator conditions can moderate the relationship between behavior and criteria, which results in a non-linear relationship between the two variables.
  • neural networks have significant advantages in the integration of information in the context of statistical judgment in comparison with competing methods of data integration.
  • they also offer the advantage that they can also be used in areas of psychology in which a sufficient theoretical knowledge base in the form of fully empirically evaluated overall models of human behavior has not yet been available are. This is especially true in the area of traffic psychology and other areas of applied psychology, such as risk research.
  • a conversion device downstream of the neural network provides for converting the result variable (s) supplied by the network into plain text information in order to further simplify the interpretation of the evaluation.
  • FIG. 1 shows the known network topology of a multilayer perceptron MLP, which receives a number of input variables E1, E2,..., En and generally several Provides output variables, here there are, for example, two output variables Ol, O 2.
  • the topology is acyclically (forward-oriented) divided into layers IL, HL, OL
  • the neurons IN1, IN2, ..., INn in a first layer, the input layer IL receive the input variables El-En as input and pass on their output signals to the neurons Hl / ... / Hm of a so-called hidden layer HL.
  • the outputs of the neurons Hl, ..., Hm of the hidden layer are connected to the neurons ON1, ON2 of the output layer OL
  • more than one hidden layer can also be provided (not shown in FIG. 1), in which case the outputs of a hidden layer are included is linked to the next hidden layer and the last hidden layer supplies the output layer.
  • the output variables Ol, O2 provided by the output layer represent the evaluation of the input data provided by the multilayer perceptron.
  • the link between the hidden layer HL and the output layer OL of FIG. 1 is described by a 2 ⁇ m matrix plus an offset value.
  • the multilayer perceptron used can have exactly one hidden layer, and it is usually advantageous if the number of neurons in the hidden layer is greater than the number of neurons in the input layer.
  • the evaluation device advantageously contains a memory for storing input data (profile vector) and result data of previous evaluations, the neural network having access to this memory, e.g. for additional training or a new training of the network.
  • the device can be set up to store the associated input and result data in the memory after evaluating a profile vector and to use the data thus supplemented in the memory as the basis for subsequent evaluations.
  • the input data include, in addition to data obtained from psychological / biometric testing, further data that were obtained from an interview with the subject concerned and / or from a biographical survey.
  • the neural network used according to the invention must be trained before use in order to “learn” the desired result to be queried.
  • the link configuration (link form and weights) of the network is based on training using data that, in addition to input data also contain probation data that were collected for the test subject in the period after an evaluation had already been carried out.
  • FIG. 3 shows the test evaluation according to the invention within the suitability test of FIG. 2.
  • the evaluation device shown below is a self-learning and self-optimizing device with an underlying method for the exact selection of the test subjects according to predetermined criteria from measurement results from recognized psychological test methods.
  • the neural network method is applied to the subject data with the help of evaluation profiles. For the mapping in the neural network, further results data from previous subjects are used for the adaptation of the network.
  • the system thus created enables the test subjects' results profiles to be optimized automatically.
  • data integration of the subject data is used with the aim of increasing the sharpness of the result data (i.e. the criterion prediction). This significantly increases the meaningfulness and reliability of the result data.
  • This method allows machine-aided evaluation of the results of psychological tests and represents a significant improvement in the sharpness of results compared to the previously dominant clinical method.
  • the measurement results of the test subject are collected in a psychological test station 1 and made available for the subsequent evaluation according to the invention in the form of raw psychological data 2.
  • data 3 relating to the test subjects are used in another way, for example interview data obtained in an interview, biographical data collected in a patient's medical history or “probation data” that were obtained through later feedback on the probation of the test subject in question.
  • These data 2, 3 are entered into a data processing unit 6, on which the analysis device according to the invention is set up using a neural network, by selecting 4 an evaluation profile (network architecture and associated weights), the data processing determines a result vector 5.
  • a memory 9 comprises a subject data memory 10 and a probation data memory 11. All available diagnostic information 12 is archived in the subject data memory 10, in particular all psychological test results 2, and, if necessary, further relevant subject-related data 2a, such as interview data and / or biographical data.
  • the probation data memory 11 manages the probation data 13.
  • the subject data fields 12 act as input data of the neural network NN. These are based on the evaluation profile 14 specified in each case according to the selection 4, which defines the type of evaluation as parameter data in the sense of the invention and in a parameter memory 15 is submitted to an evaluation. This results in the result vector 5 which provides all the relevant data of the subject.
  • the evaluation profile 14 is the characteristic values of the weights of the links within the relevant neural network. Each evaluation profile 14 was learned for a given network structure based on a training sarple. The evaluation profile enables simple and quick selection of differently trained networks according to the given requirements. All evaluation profiles are held in the parameter memory 15, which is expediently also part of the memory 9.
  • the data in the memory 14 are available to the neural network for its initialization, in particular for training.
  • the associated data are recorded in the subject data memory 10; the same applies to the probation data memory 11 if new events become known to test subjects.
  • probation data probation data memory 11 belonging to the sample are used.
  • a major advantage of the arrangement is the application of the self-learning neural network to the subject data on the basis of further information (probation data) about the same. This data is used for further learning of the network.
  • the sharpness (the information security) of the results can be controlled iteratively significantly compared to all other known methods.
  • the selection of which of the available data are used for training the evaluation device according to the invention or for input in a specific test subject assessment differs from that described here.
  • Probation data can also be used as input data for the assessment, if such data is available, or parts of the data such as test or interview data can be reserved for training.
  • driver suitability of driver candidates who are considered to be risky (eg due to previous conspicuous behavior, criminal offenses or the like) is assessed.
  • the characteristic values of five were used as the data basis for the classification of drivers various performance tests in the area of responsiveness and perception, age and the assignment of people with regard to their fitness to drive based on the driving style or a global judgment of driving behavior.
  • the data were collected in a multi-center validation study (see Karner & Neuwirth, 2000; summer , 2001)
  • the driving style of the study participants was determined using a hierarchical cluster analysis based on the Ward algorithm, based on the assessment of individual dimensions of safe driving behavior during a standardized driving test.
  • the global judgment provides an overall impression of driving behavior from a traffic perspective psychologists.
  • the variables used as input data for the evaluation are z-shaped with respect to the mean and the standard deviation of the underlying distribution.
  • the mean value of the distribution is subtracted from each value, and the associated z-transformed value is obtained by subsequent division by the standard deviation.
  • the z-transformation ensures that the characteristic values used - regardless of their initial value range - assume a comparable value range. This is especially so when combining reaction times (typically of the order of magnitude several 100 ms) and numbers of correct answers from test procedures with different number of items of importance.
  • the test subjects' z-transformed test results in the areas of responsiveness and perception, as well as their age, are the predictors.
  • the assessment of fitness to drive based on the criterion driving style or overall judgment in suitable and unsuitable drivers form the two criterion variables for which a separate neural network was created and trained. So two different neural networks were trained, which only differ in the criteria variable used (global judgment vs. driving style) - and thus in the associated evaluation profile.
  • the two output neurons correspond to the two possible forms of the dichotomous criterion (e.g. suitable or unsuitable).
  • dichotomous criterion e.g. suitable or unsuitable.
  • multi-category criterion variables are also conceivable, e.g. different professions or uses; in such a case, a corresponding number of output neurons would have to be provided.
  • the neural networks used were implemented on a PC using a commercial software package (Math Lab).
  • a network was implemented in which each unit of the preceding layer is connected to each unit of the subsequent layer (so-called full feed forwarding; only indicated in FIG. 1 for the sake of clarity).
  • the well-known Softmax function is used as a transfer function in order to standardize the output variables to 100%.
  • the well-known back propagation algorithm was used for the learning algorithm.
  • the network structure was defined in the manner described here in order to be able to map possible nonlinear effects with good generalizability of the results.
  • the number of units in the hidden layer must be large enough to accomplish the task at hand, but also small enough to enable a sufficiently good generalization of the network.
  • the number of neurons in the hidden layer was systematically varied in several calculations using the same data sets and the results of the different which networks are compared with each other for one of the criteria regarding generalization ability and classification rate. An optimal number of neurons in the hidden layer was determined from the results obtained in this way.
  • the generalizability was checked using the method of partial sampling, whereby 10 randomly selected persons acted as test samples for each of the two criteria, on the basis of which the generalizability of the network, which was now trained again with the now reduced sample size, was checked.
  • the results on the generalizability of the results indicate good stability of the results obtained.
  • a classification rate of 86 percent correct assignments could be achieved for the criterion global judgment.
  • the classification rate was 90 percent of correct assignments, with correctly classified persons with 63 to 99 percent certainty assigned correctly, while the safety of incorrect assignments was 60 percent.
  • the good agreement between the classification rate in the learning sample (86.3%) and test sample (90%) indicates a good network specification.
  • the second area of application was 92 complete data of a test battery consisting of seven different test methods of a validation study for the selection of successful graduates of flight training from an Austrian airline (Lauda Airlines), as well as information about the successful completion or termination of the training.
  • a multilayer perceptron consisting of a total of three layers was also used.
  • the network consisted of eleven input units to represent the z-transformed raw values from the individual test procedures, fifteen units of the hidden layer and the two output layer units to represent the two characteristics of the criterion variable, educational success.
  • the evaluation of the data according to the invention provided a surprisingly high level of reliability.
  • the classification rate was 80 percent of correct assignments, with correctly classified people with 60 to 99 percent certainty assigned correctly, while the safety of the wrong assignment was between 63 to 68 percent.
  • the agreement between the classification rate in the learning sample (93.6%) and test sample (80%) can be described as sufficiently good.
  • the generalization of the results of the neural network can also be assessed as satisfactory here.
  • neural networks have several advantages compared to conventional methods of data integration.
  • a major advantage is the comparatively low data requirements.
  • neural networks also allow adequate consideration of non-linear relationships between the predictor variable and the selected criterion variable, which cannot be achieved by a human diagnostician.

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  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Medical Informatics (AREA)
  • Public Health (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Pathology (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)

Abstract

L'invention concerne un dispositif et un procédé permettant l'évaluation de données brutes psychologiques et/ou biomédicales sous forme d'un vecteur profil comprenant des données relatives à un sujet, obtenues dans le cadre de tests psychologiques/biomédicaux mais provenant toutefois, au moins en partie, de tests purement psychologiques -. Le dispositif selon l'invention est caractérisé en ce qu'il comprend un réseau neuronique (NN) dans lequel on peut introduire, en tant que données d'entrée, les données (12) d'un vecteur profil, ainsi que des données de paramètres (14) pour la sélection de grandeurs devant être dérivées, en ce que le réseau neuronique est agencé pour la sortie d'au moins une grandeur de résultat (5) dérivée des données d'entrée conformément aux données des paramètres, et en ce que ledit résultat est utilisé pour l'évaluation de l'état du sujet.
EP02778969A 2001-10-09 2002-10-08 Dispositif d'evaluation de donnees brutes psychologiques et biomedicales Withdrawn EP1434516A2 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
AT15912001 2001-10-09
AT0159101A AT411143B (de) 2001-10-09 2001-10-09 Vorrichtung zum auswerten psychologischer und biomedizinischer rohdaten
PCT/AT2002/000289 WO2003030705A2 (fr) 2001-10-09 2002-10-08 Dispositif d'evaluation de donnees brutes psychologiques et biomedicales

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EP1434516A2 true EP1434516A2 (fr) 2004-07-07

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WO (1) WO2003030705A2 (fr)

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CN113593674B (zh) * 2020-04-30 2024-05-31 北京心数矩阵科技有限公司 一种基于结构化神经网络的性格影响因子分析方法
DE102021112662A1 (de) 2021-05-17 2022-11-17 Carmen Held Auf einer elektronischen Datenverarbeitungsanlage implementiertes Expertensystem zur Diagnostik einer Eignung zum Arbeiten im Homeoffice insbesondere von Arbeitnehmern

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SOMERS M.J.: "Thinking differently: assessing nonlinearities in the relationship between work attitudes and job performance using a Bayesian neural network", JOURNAL OF OCCUPATIONAL AND ORGANIZATIONAL PSYCHOLOGY, vol. 74, March 2001 (2001-03-01), pages 47 - 61, XP008053928 *

Also Published As

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WO2003030705A2 (fr) 2003-04-17
AT411143B (de) 2003-10-27
WO2003030705A3 (fr) 2003-09-12
ATA15912001A (de) 2003-03-15

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