Cargando…

Machine Learning Based Linking of Patient Reported Outcome Measures to WHO International Classification of Functioning, Disability, and Health Activity/Participation Categories

Background: In the primary and secondary medical health sector, patient reported outcome measures (PROMs) are widely used to assess a patient’s disease-related functional health state. However, the World Health Organization (WHO), in its recently adopted resolution on “strengthening rehabilitation i...

Descripción completa

Detalles Bibliográficos
Autores principales: Habenicht, Richard, Fehrmann, Elisabeth, Blohm, Peter, Ebenbichler, Gerold, Fischer-Grote, Linda, Kollmitzer, Josef, Mair, Patrick, Kienbacher, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10488436/
https://www.ncbi.nlm.nih.gov/pubmed/37685676
http://dx.doi.org/10.3390/jcm12175609
_version_ 1785103475136266240
author Habenicht, Richard
Fehrmann, Elisabeth
Blohm, Peter
Ebenbichler, Gerold
Fischer-Grote, Linda
Kollmitzer, Josef
Mair, Patrick
Kienbacher, Thomas
author_facet Habenicht, Richard
Fehrmann, Elisabeth
Blohm, Peter
Ebenbichler, Gerold
Fischer-Grote, Linda
Kollmitzer, Josef
Mair, Patrick
Kienbacher, Thomas
author_sort Habenicht, Richard
collection PubMed
description Background: In the primary and secondary medical health sector, patient reported outcome measures (PROMs) are widely used to assess a patient’s disease-related functional health state. However, the World Health Organization (WHO), in its recently adopted resolution on “strengthening rehabilitation in all health systems”, encourages that all health sectors, not only the rehabilitation sector, classify a patient’s functioning and health state according to the International Classification of Functioning, Disability and Health (ICF). Aim: This research sought to optimize machine learning (ML) methods that fully and automatically link information collected from PROMs in persons with unspecific chronic low back pain (cLBP) to limitations in activities and restrictions in participation that are listed in the WHO core set categories for LBP. The study also aimed to identify the minimal set of PROMs necessary for linking without compromising performance. Methods: A total of 806 patients with cLBP completed a comprehensive set of validated PROMs and were interviewed by clinical psychologists who assessed patients’ performance in activity limitations and restrictions in participation according to the ICF brief core set for low back pain (LBP). The information collected was then utilized to further develop random forest (RF) methods that classified the presence or absence of a problem within each of the activity participation ICF categories of the ICF core set for LBP. Further analyses identified those PROM items relevant to the linking process and validated the respective linking performance that utilized a minimal subset of items. Results: Compared to a recently developed ML linking method, receiver operating characteristic curve (ROC-AUC) values for the novel RF methods showed overall improved performance, with AUC values ranging from 0.73 for the ICF category d850 to 0.81 for the ICF category d540. Variable importance measurements revealed that minimal subsets of either 24 or 15 important PROM variables (out of 80 items included in full set of PROMs) would show similar linking performance. Conclusions: Findings suggest that our optimized ML based methods more accurately predict the presence or absence of limitations and restrictions listed in ICF core categories for cLBP. In addition, this accurate performance would not suffer if the list of PROM items was reduced to a minimum of 15 out of 80 items assessed.
format Online
Article
Text
id pubmed-10488436
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-104884362023-09-09 Machine Learning Based Linking of Patient Reported Outcome Measures to WHO International Classification of Functioning, Disability, and Health Activity/Participation Categories Habenicht, Richard Fehrmann, Elisabeth Blohm, Peter Ebenbichler, Gerold Fischer-Grote, Linda Kollmitzer, Josef Mair, Patrick Kienbacher, Thomas J Clin Med Article Background: In the primary and secondary medical health sector, patient reported outcome measures (PROMs) are widely used to assess a patient’s disease-related functional health state. However, the World Health Organization (WHO), in its recently adopted resolution on “strengthening rehabilitation in all health systems”, encourages that all health sectors, not only the rehabilitation sector, classify a patient’s functioning and health state according to the International Classification of Functioning, Disability and Health (ICF). Aim: This research sought to optimize machine learning (ML) methods that fully and automatically link information collected from PROMs in persons with unspecific chronic low back pain (cLBP) to limitations in activities and restrictions in participation that are listed in the WHO core set categories for LBP. The study also aimed to identify the minimal set of PROMs necessary for linking without compromising performance. Methods: A total of 806 patients with cLBP completed a comprehensive set of validated PROMs and were interviewed by clinical psychologists who assessed patients’ performance in activity limitations and restrictions in participation according to the ICF brief core set for low back pain (LBP). The information collected was then utilized to further develop random forest (RF) methods that classified the presence or absence of a problem within each of the activity participation ICF categories of the ICF core set for LBP. Further analyses identified those PROM items relevant to the linking process and validated the respective linking performance that utilized a minimal subset of items. Results: Compared to a recently developed ML linking method, receiver operating characteristic curve (ROC-AUC) values for the novel RF methods showed overall improved performance, with AUC values ranging from 0.73 for the ICF category d850 to 0.81 for the ICF category d540. Variable importance measurements revealed that minimal subsets of either 24 or 15 important PROM variables (out of 80 items included in full set of PROMs) would show similar linking performance. Conclusions: Findings suggest that our optimized ML based methods more accurately predict the presence or absence of limitations and restrictions listed in ICF core categories for cLBP. In addition, this accurate performance would not suffer if the list of PROM items was reduced to a minimum of 15 out of 80 items assessed. MDPI 2023-08-28 /pmc/articles/PMC10488436/ /pubmed/37685676 http://dx.doi.org/10.3390/jcm12175609 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Habenicht, Richard
Fehrmann, Elisabeth
Blohm, Peter
Ebenbichler, Gerold
Fischer-Grote, Linda
Kollmitzer, Josef
Mair, Patrick
Kienbacher, Thomas
Machine Learning Based Linking of Patient Reported Outcome Measures to WHO International Classification of Functioning, Disability, and Health Activity/Participation Categories
title Machine Learning Based Linking of Patient Reported Outcome Measures to WHO International Classification of Functioning, Disability, and Health Activity/Participation Categories
title_full Machine Learning Based Linking of Patient Reported Outcome Measures to WHO International Classification of Functioning, Disability, and Health Activity/Participation Categories
title_fullStr Machine Learning Based Linking of Patient Reported Outcome Measures to WHO International Classification of Functioning, Disability, and Health Activity/Participation Categories
title_full_unstemmed Machine Learning Based Linking of Patient Reported Outcome Measures to WHO International Classification of Functioning, Disability, and Health Activity/Participation Categories
title_short Machine Learning Based Linking of Patient Reported Outcome Measures to WHO International Classification of Functioning, Disability, and Health Activity/Participation Categories
title_sort machine learning based linking of patient reported outcome measures to who international classification of functioning, disability, and health activity/participation categories
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10488436/
https://www.ncbi.nlm.nih.gov/pubmed/37685676
http://dx.doi.org/10.3390/jcm12175609
work_keys_str_mv AT habenichtrichard machinelearningbasedlinkingofpatientreportedoutcomemeasurestowhointernationalclassificationoffunctioningdisabilityandhealthactivityparticipationcategories
AT fehrmannelisabeth machinelearningbasedlinkingofpatientreportedoutcomemeasurestowhointernationalclassificationoffunctioningdisabilityandhealthactivityparticipationcategories
AT blohmpeter machinelearningbasedlinkingofpatientreportedoutcomemeasurestowhointernationalclassificationoffunctioningdisabilityandhealthactivityparticipationcategories
AT ebenbichlergerold machinelearningbasedlinkingofpatientreportedoutcomemeasurestowhointernationalclassificationoffunctioningdisabilityandhealthactivityparticipationcategories
AT fischergrotelinda machinelearningbasedlinkingofpatientreportedoutcomemeasurestowhointernationalclassificationoffunctioningdisabilityandhealthactivityparticipationcategories
AT kollmitzerjosef machinelearningbasedlinkingofpatientreportedoutcomemeasurestowhointernationalclassificationoffunctioningdisabilityandhealthactivityparticipationcategories
AT mairpatrick machinelearningbasedlinkingofpatientreportedoutcomemeasurestowhointernationalclassificationoffunctioningdisabilityandhealthactivityparticipationcategories
AT kienbacherthomas machinelearningbasedlinkingofpatientreportedoutcomemeasurestowhointernationalclassificationoffunctioningdisabilityandhealthactivityparticipationcategories