Analysing functional measurements and questionnaire results for automated back-pain disorder classification

Mark van Gils, Juha Pärkkä, Jukka Suovanen

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

Abstract

There is a strong need to develop a system that objectively and automatically interprets examination re-sults for people with potential back-pain problems. The right suggestion for a suitable exercise or reha-bilitation program in an early phase instead of having a patient make a number of consultations and ex-aminations can save large amounts of money to the healthcare system. Much of the knowledge used to make a correct diagnosis is implicitly present in the clinician's experience. Therefore it has proven to be difficult to use straightforward rules or algorithms to automate this task. In this study we aimed to de-velop classifiers using physical-measurement oriented data sets as well data consisting of answers to questionnaires concerning lifestyle, subjective experiences and emotional states. Statistical analyses were performed to find information shared between variables and reduce the feature set. Linear dis-criminant classifiers, logistic regression classifiers and artificial neural networks were used to develop a system that is able to indicate whether or not a person would need further examination because of poten-tial back-pain problems. The problem appears to be too complex for a linear approach, non-linear ap-proaches like artificial neural networks and logistic regression classifiers prove to be suitable for this problem.
Original languageEnglish
Title of host publicationProceedings of the Xth Mediterranean Conference on Medical and Biological Engineering. Ischia, Naples, Italy 2004
Subtitle of host publicationMEDICON 2004
Publication statusPublished - 2004
MoE publication typeB3 Non-refereed article in conference proceedings
EventXth Mediterranean Conference on Medical and Biological Engineering
and Computing, MEDICON 2004
- Naples, Italy
Duration: 31 Jul 20045 Aug 2004

Conference

ConferenceXth Mediterranean Conference on Medical and Biological Engineering
and Computing, MEDICON 2004
CountryItaly
CityNaples
Period31/07/045/08/04

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Classifiers
Logistics
Neural networks
Amination

Cite this

van Gils, M., Pärkkä, J., & Suovanen, J. (2004). Analysing functional measurements and questionnaire results for automated back-pain disorder classification. In Proceedings of the Xth Mediterranean Conference on Medical and Biological Engineering. Ischia, Naples, Italy 2004: MEDICON 2004
van Gils, Mark ; Pärkkä, Juha ; Suovanen, Jukka. / Analysing functional measurements and questionnaire results for automated back-pain disorder classification. Proceedings of the Xth Mediterranean Conference on Medical and Biological Engineering. Ischia, Naples, Italy 2004: MEDICON 2004. 2004.
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van Gils, M, Pärkkä, J & Suovanen, J 2004, Analysing functional measurements and questionnaire results for automated back-pain disorder classification. in Proceedings of the Xth Mediterranean Conference on Medical and Biological Engineering. Ischia, Naples, Italy 2004: MEDICON 2004. Xth Mediterranean Conference on Medical and Biological Engineering
and Computing, MEDICON 2004, Naples, Italy, 31/07/04.

Analysing functional measurements and questionnaire results for automated back-pain disorder classification. / van Gils, Mark; Pärkkä, Juha; Suovanen, Jukka.

Proceedings of the Xth Mediterranean Conference on Medical and Biological Engineering. Ischia, Naples, Italy 2004: MEDICON 2004. 2004.

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

TY - GEN

T1 - Analysing functional measurements and questionnaire results for automated back-pain disorder classification

AU - van Gils, Mark

AU - Pärkkä, Juha

AU - Suovanen, Jukka

N1 - HUO: CD-rom Project code: T2SU00164

PY - 2004

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N2 - There is a strong need to develop a system that objectively and automatically interprets examination re-sults for people with potential back-pain problems. The right suggestion for a suitable exercise or reha-bilitation program in an early phase instead of having a patient make a number of consultations and ex-aminations can save large amounts of money to the healthcare system. Much of the knowledge used to make a correct diagnosis is implicitly present in the clinician's experience. Therefore it has proven to be difficult to use straightforward rules or algorithms to automate this task. In this study we aimed to de-velop classifiers using physical-measurement oriented data sets as well data consisting of answers to questionnaires concerning lifestyle, subjective experiences and emotional states. Statistical analyses were performed to find information shared between variables and reduce the feature set. Linear dis-criminant classifiers, logistic regression classifiers and artificial neural networks were used to develop a system that is able to indicate whether or not a person would need further examination because of poten-tial back-pain problems. The problem appears to be too complex for a linear approach, non-linear ap-proaches like artificial neural networks and logistic regression classifiers prove to be suitable for this problem.

AB - There is a strong need to develop a system that objectively and automatically interprets examination re-sults for people with potential back-pain problems. The right suggestion for a suitable exercise or reha-bilitation program in an early phase instead of having a patient make a number of consultations and ex-aminations can save large amounts of money to the healthcare system. Much of the knowledge used to make a correct diagnosis is implicitly present in the clinician's experience. Therefore it has proven to be difficult to use straightforward rules or algorithms to automate this task. In this study we aimed to de-velop classifiers using physical-measurement oriented data sets as well data consisting of answers to questionnaires concerning lifestyle, subjective experiences and emotional states. Statistical analyses were performed to find information shared between variables and reduce the feature set. Linear dis-criminant classifiers, logistic regression classifiers and artificial neural networks were used to develop a system that is able to indicate whether or not a person would need further examination because of poten-tial back-pain problems. The problem appears to be too complex for a linear approach, non-linear ap-proaches like artificial neural networks and logistic regression classifiers prove to be suitable for this problem.

M3 - Conference article in proceedings

SN - 88-7780-308-8

BT - Proceedings of the Xth Mediterranean Conference on Medical and Biological Engineering. Ischia, Naples, Italy 2004

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van Gils M, Pärkkä J, Suovanen J. Analysing functional measurements and questionnaire results for automated back-pain disorder classification. In Proceedings of the Xth Mediterranean Conference on Medical and Biological Engineering. Ischia, Naples, Italy 2004: MEDICON 2004. 2004