Umeå University's logo

umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Natural language processing to classify caregiver strategies supporting participation among children and youth with craniofacial microsomia and other childhood-onset disabilities
Department of Occupational Therapy, University of Illinois Chicago, 1919 West Taylor Street, Room 316A, Chicago, IL 60612−7250, USA; Department of Computer Science, University of Illinois Chicago, 851 South Morgan Street, Room 1132, Chicago, IL 60607-7042, USA; Children’s Participation in Environment Research Lab, University of Illinois Chicago, Chicago, IL, USA.ORCID iD: 0000-0003-1290-9441
Show others and affiliations
2023 (English)In: Journal of Healthcare Informatics Research, ISSN 2509-4971, E-ISSN 2509-498XArticle in journal (Refereed) Epub ahead of print
Abstract [en]

Customizing participation-focused pediatric rehabilitation interventions is an important but also complex and potentially resource intensive process, which may benefit from automated and simplified steps. This research aimed at applying natural language processing to develop and identify a best performing predictive model that classifies caregiver strategies into participation-related constructs, while filtering out non-strategies. We created a dataset including 1,576 caregiver strategies obtained from 236 families of children and youth (11–17 years) with craniofacial microsomia or other childhood-onset disabilities. These strategies were annotated to four participation-related constructs and a non-strategy class. We experimented with manually created features (i.e., speech and dependency tags, predefined likely sets of words, dense lexicon features (i.e., Unified Medical Language System (UMLS) concepts)) and three classical methods (i.e., logistic regression, naïve Bayes, support vector machines (SVM)). We tested a series of binary and multinomial classification tasks applying 10-fold cross-validation on the training set (80%) to test the best performing model on the held-out test set (20%). SVM using term frequency-inverse document frequency (TF-IDF) was the best performing model for all four classification tasks, with accuracy ranging from 78.10 to 94.92% and a macro-averaged F1-score ranging from 0.58 to 0.83. Manually created features only increased model performance when filtering out non-strategies. Results suggest pipelined classification tasks (i.e., filtering out non-strategies; classification into intrinsic and extrinsic strategies; classification into participation-related constructs) for implementation into participation-focused pediatric rehabilitation interventions like Participation and Environment Measure Plus (PEM+) among caregivers who complete the Participation and Environment Measure for Children and Youth (PEM-CY). 

Place, publisher, year, edition, pages
Springer Nature, 2023.
Keywords [en]
pediatric rehabilitation, artificial intelligence, activities, preferences, sense of self, environment
National Category
Occupational Therapy Other Health Sciences Engineering and Technology
Identifiers
URN: urn:nbn:se:umu:diva-214632DOI: 10.1007/s41666-023-00149-yOAI: oai:DiVA.org:umu-214632DiVA, id: diva2:1799106
Available from: 2023-09-21 Created: 2023-09-21 Last updated: 2023-09-21

Open Access in DiVA

fulltext(1131 kB)22 downloads
File information
File name FULLTEXT01.pdfFile size 1131 kBChecksum SHA-512
16ee96cbccb4c352b4fce70223d8718351156a9f3b001d9a47bd0f1a5dc571903bfe5c96fcda3f3065dd6af17a5aaf32c5c2a1e3350db78744b4046c4f948479
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records

Kaelin, Vera C.

Search in DiVA

By author/editor
Kaelin, Vera C.Boyd, Andrew D.Werler, Martha M.Parde, NatalieKhetani, Mary A.
In the same journal
Journal of Healthcare Informatics Research
Occupational TherapyOther Health SciencesEngineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 22 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 166 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf