Limits...
Using machine learning algorithms to guide rehabilitation planning for home care clients.

Zhu M, Zhang Z, Hirdes JP, Stolee P - BMC Med Inform Decis Mak (2007)

Bottom Line: SVM and KNN results are compared with those obtained using the ADLCAP.Machine learning algorithms achieved superior predictions than the current protocol.Machine learning results are less readily interpretable, but can also be used to guide development of improved clinical protocols.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Health Studies and Gerontology, University of Waterloo, Waterloo, ON, Canada. m3zhu@uwaterloo.ca

ABSTRACT

Background: Targeting older clients for rehabilitation is a clinical challenge and a research priority. We investigate the potential of machine learning algorithms - Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) - to guide rehabilitation planning for home care clients.

Methods: This study is a secondary analysis of data on 24,724 longer-term clients from eight home care programs in Ontario. Data were collected with the RAI-HC assessment system, in which the Activities of Daily Living Clinical Assessment Protocol (ADLCAP) is used to identify clients with rehabilitation potential. For study purposes, a client is defined as having rehabilitation potential if there was: i) improvement in ADL functioning, or ii) discharge home. SVM and KNN results are compared with those obtained using the ADLCAP. For comparison, the machine learning algorithms use the same functional and health status indicators as the ADLCAP.

Results: The KNN and SVM algorithms achieved similar substantially improved performance over the ADLCAP, although false positive and false negative rates were still fairly high (FP > .18, FN > .34 versus FP > .29, FN. > .58 for ADLCAP). Results are used to suggest potential revisions to the ADLCAP.

Conclusion: Machine learning algorithms achieved superior predictions than the current protocol. Machine learning results are less readily interpretable, but can also be used to guide development of improved clinical protocols.

Show MeSH
Overall error rates from 10 simulation experiments. KNN and SVM perform comparably with recoded data. KNN performs slightly worse whereas SVM performs slightly better with original data.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2235834&req=5

Figure 3: Overall error rates from 10 simulation experiments. KNN and SVM perform comparably with recoded data. KNN performs slightly worse whereas SVM performs slightly better with original data.

Mentions: The overall error rates of KNN and SVM from the 10 simulations are shown using boxplots in Figure 3. The performances of KNN and SVM are almost identical when applied to the recoded variables, but when applied to the original variables, SVM performs slightly better whereas KNN performs slightly worse.


Using machine learning algorithms to guide rehabilitation planning for home care clients.

Zhu M, Zhang Z, Hirdes JP, Stolee P - BMC Med Inform Decis Mak (2007)

Overall error rates from 10 simulation experiments. KNN and SVM perform comparably with recoded data. KNN performs slightly worse whereas SVM performs slightly better with original data.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2235834&req=5

Figure 3: Overall error rates from 10 simulation experiments. KNN and SVM perform comparably with recoded data. KNN performs slightly worse whereas SVM performs slightly better with original data.
Mentions: The overall error rates of KNN and SVM from the 10 simulations are shown using boxplots in Figure 3. The performances of KNN and SVM are almost identical when applied to the recoded variables, but when applied to the original variables, SVM performs slightly better whereas KNN performs slightly worse.

Bottom Line: SVM and KNN results are compared with those obtained using the ADLCAP.Machine learning algorithms achieved superior predictions than the current protocol.Machine learning results are less readily interpretable, but can also be used to guide development of improved clinical protocols.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Health Studies and Gerontology, University of Waterloo, Waterloo, ON, Canada. m3zhu@uwaterloo.ca

ABSTRACT

Background: Targeting older clients for rehabilitation is a clinical challenge and a research priority. We investigate the potential of machine learning algorithms - Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) - to guide rehabilitation planning for home care clients.

Methods: This study is a secondary analysis of data on 24,724 longer-term clients from eight home care programs in Ontario. Data were collected with the RAI-HC assessment system, in which the Activities of Daily Living Clinical Assessment Protocol (ADLCAP) is used to identify clients with rehabilitation potential. For study purposes, a client is defined as having rehabilitation potential if there was: i) improvement in ADL functioning, or ii) discharge home. SVM and KNN results are compared with those obtained using the ADLCAP. For comparison, the machine learning algorithms use the same functional and health status indicators as the ADLCAP.

Results: The KNN and SVM algorithms achieved similar substantially improved performance over the ADLCAP, although false positive and false negative rates were still fairly high (FP > .18, FN > .34 versus FP > .29, FN. > .58 for ADLCAP). Results are used to suggest potential revisions to the ADLCAP.

Conclusion: Machine learning algorithms achieved superior predictions than the current protocol. Machine learning results are less readily interpretable, but can also be used to guide development of improved clinical protocols.

Show MeSH