A neural network-based method for automated assessment of well-being based on personnel screening questionnaires

Mark van Gils, Jukka Suovanen, Juho Merilahti, Juha Pärkkä, Henri Rautamo

Research output: Chapter in Book/Report/Conference proceedingConference abstract in proceedingsScientific

Abstract

There is a need for objective, simple methods to assess employees' well-being. Currently, a healthcare professional assesses this partly using experience-based knowledge, making it difficult to use explicit rules to automate this task. We aimed to develop classifiers learning from data from questions in personnel screening questionnaires. Our goals were: a) to define an efficient subset of questions and b) to develop a classifier for well-being grading. Screening data from occupational health checks in a random selection of the working population in various parts of Finland were used. Data contained 98 answers on medication usage, pain experience, psychological factors, stress, lifestyle etc. A healthcare professional scored well-being on a scale from 1 to 3 (indicating seriousness in decrease of well-being). 1063 subjects were used. For a), relationships between variables were assessed using correspondence analyses. For b), linear and non-linear regression and neural networks (backpropagation networks) were used with as input the variables found from a) and as desired output the well-being scores. Final performance was assessed using an independent test set of 89 subjects. Significant correspondences between different questions were found. A subset of 9 independent questions proved to be efficient. These questions relate to weight, dizziness, sports activities, pain experience, views on life, and personal assessment of the ability to continue work. A neural network gave the best results with an accuracy of 83% on the test set, and sensitivity 85% and specificity 83% for separating 'reduced' from 'no reduced' well-being. It is possible to construct a classifier for severity of decrease in well-being that is in good agreement with a healthcare professional's opinion. That performance can be obtained with a relatively small number of questions. This allows implementation of a classifier in quick-to-complete questionnaires that can be used routinely.
Original languageEnglish
Title of host publicationProceedings of the 29th ICOH, International Congress on Occupational Health. Cape Town, South Africa, 22 - 27 March 2009
Pages539
Publication statusPublished - 2009
MoE publication typeNot Eligible
Event29th ICOH, International Congress on Occupational Health - Cape Town, South Africa
Duration: 22 Mar 200927 Mar 2009

Conference

Conference29th ICOH, International Congress on Occupational Health
CountrySouth Africa
CityCape Town
Period22/03/0927/03/09

Fingerprint

Delivery of Health Care
Pain
Aptitude
Dizziness
Occupational Health
Finland
Psychological Stress
Sports
Life Style
Learning
Psychology
Weights and Measures
Sensitivity and Specificity
Population
Surveys and Questionnaires

Keywords

  • personnel screening
  • well-being
  • neural networks

Cite this

van Gils, M., Suovanen, J., Merilahti, J., Pärkkä, J., & Rautamo, H. (2009). A neural network-based method for automated assessment of well-being based on personnel screening questionnaires. In Proceedings of the 29th ICOH, International Congress on Occupational Health. Cape Town, South Africa, 22 - 27 March 2009 (pp. 539)
van Gils, Mark ; Suovanen, Jukka ; Merilahti, Juho ; Pärkkä, Juha ; Rautamo, Henri. / A neural network-based method for automated assessment of well-being based on personnel screening questionnaires. Proceedings of the 29th ICOH, International Congress on Occupational Health. Cape Town, South Africa, 22 - 27 March 2009. 2009. pp. 539
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van Gils, M, Suovanen, J, Merilahti, J, Pärkkä, J & Rautamo, H 2009, A neural network-based method for automated assessment of well-being based on personnel screening questionnaires. in Proceedings of the 29th ICOH, International Congress on Occupational Health. Cape Town, South Africa, 22 - 27 March 2009. pp. 539, 29th ICOH, International Congress on Occupational Health, Cape Town, South Africa, 22/03/09.

A neural network-based method for automated assessment of well-being based on personnel screening questionnaires. / van Gils, Mark; Suovanen, Jukka; Merilahti, Juho; Pärkkä, Juha; Rautamo, Henri.

Proceedings of the 29th ICOH, International Congress on Occupational Health. Cape Town, South Africa, 22 - 27 March 2009. 2009. p. 539.

Research output: Chapter in Book/Report/Conference proceedingConference abstract in proceedingsScientific

TY - CHAP

T1 - A neural network-based method for automated assessment of well-being based on personnel screening questionnaires

AU - van Gils, Mark

AU - Suovanen, Jukka

AU - Merilahti, Juho

AU - Pärkkä, Juha

AU - Rautamo, Henri

N1 - Project code: 22275

PY - 2009

Y1 - 2009

N2 - There is a need for objective, simple methods to assess employees' well-being. Currently, a healthcare professional assesses this partly using experience-based knowledge, making it difficult to use explicit rules to automate this task. We aimed to develop classifiers learning from data from questions in personnel screening questionnaires. Our goals were: a) to define an efficient subset of questions and b) to develop a classifier for well-being grading. Screening data from occupational health checks in a random selection of the working population in various parts of Finland were used. Data contained 98 answers on medication usage, pain experience, psychological factors, stress, lifestyle etc. A healthcare professional scored well-being on a scale from 1 to 3 (indicating seriousness in decrease of well-being). 1063 subjects were used. For a), relationships between variables were assessed using correspondence analyses. For b), linear and non-linear regression and neural networks (backpropagation networks) were used with as input the variables found from a) and as desired output the well-being scores. Final performance was assessed using an independent test set of 89 subjects. Significant correspondences between different questions were found. A subset of 9 independent questions proved to be efficient. These questions relate to weight, dizziness, sports activities, pain experience, views on life, and personal assessment of the ability to continue work. A neural network gave the best results with an accuracy of 83% on the test set, and sensitivity 85% and specificity 83% for separating 'reduced' from 'no reduced' well-being. It is possible to construct a classifier for severity of decrease in well-being that is in good agreement with a healthcare professional's opinion. That performance can be obtained with a relatively small number of questions. This allows implementation of a classifier in quick-to-complete questionnaires that can be used routinely.

AB - There is a need for objective, simple methods to assess employees' well-being. Currently, a healthcare professional assesses this partly using experience-based knowledge, making it difficult to use explicit rules to automate this task. We aimed to develop classifiers learning from data from questions in personnel screening questionnaires. Our goals were: a) to define an efficient subset of questions and b) to develop a classifier for well-being grading. Screening data from occupational health checks in a random selection of the working population in various parts of Finland were used. Data contained 98 answers on medication usage, pain experience, psychological factors, stress, lifestyle etc. A healthcare professional scored well-being on a scale from 1 to 3 (indicating seriousness in decrease of well-being). 1063 subjects were used. For a), relationships between variables were assessed using correspondence analyses. For b), linear and non-linear regression and neural networks (backpropagation networks) were used with as input the variables found from a) and as desired output the well-being scores. Final performance was assessed using an independent test set of 89 subjects. Significant correspondences between different questions were found. A subset of 9 independent questions proved to be efficient. These questions relate to weight, dizziness, sports activities, pain experience, views on life, and personal assessment of the ability to continue work. A neural network gave the best results with an accuracy of 83% on the test set, and sensitivity 85% and specificity 83% for separating 'reduced' from 'no reduced' well-being. It is possible to construct a classifier for severity of decrease in well-being that is in good agreement with a healthcare professional's opinion. That performance can be obtained with a relatively small number of questions. This allows implementation of a classifier in quick-to-complete questionnaires that can be used routinely.

KW - personnel screening

KW - well-being

KW - neural networks

M3 - Conference abstract in proceedings

SP - 539

BT - Proceedings of the 29th ICOH, International Congress on Occupational Health. Cape Town, South Africa, 22 - 27 March 2009

ER -

van Gils M, Suovanen J, Merilahti J, Pärkkä J, Rautamo H. A neural network-based method for automated assessment of well-being based on personnel screening questionnaires. In Proceedings of the 29th ICOH, International Congress on Occupational Health. Cape Town, South Africa, 22 - 27 March 2009. 2009. p. 539