Abstract
| Original language | Undefined |
|---|---|
| Title of host publication | Proceedings of the 4th International Workshop on Web Intelligence & Communities |
| Place of Publication | New York |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 48-54 |
| Number of pages | 7 |
| ISBN (Print) | 978-1-4503-1189-2 |
| DOIs | |
| Publication status | Published - 16 Apr 2012 |
Publication series
| Name | |
|---|---|
| Publisher | ACM |
Keywords
- METIS-286328
- Decision support
- EWI-21771
- IR-80215
- Homecare
- Machine Learning
Cite this
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Towards Decision Support for a Homecare Services Platform. / van 't Klooster, J.W.J.R.; Combes, Catherine; van Beijnum, Bernhard J.F.
Proceedings of the 4th International Workshop on Web Intelligence & Communities. New York : Association for Computing Machinery (ACM), 2012. p. 48-54.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Towards Decision Support for a Homecare Services Platform
AU - van 't Klooster, J.W.J.R.
AU - Combes, Catherine
AU - van Beijnum, Bernhard J.F.
N1 - 10.1145/2189736.2189747
PY - 2012/4/16
Y1 - 2012/4/16
N2 - It is believed that ICT-mediation for home care services increases patient empowerment, independency, self-efficacy and quality of life. Providing elderly people with tailored care services allows us to learn from patient data to predict future care needs. In this article, we demonstrate the contribution of machine learning to homecare services, using data collected by a home care services platform. As an actual case, we show how simulated medication compliance can be measured and modeled using clustering and regression techniques. The approach is validated using data from French nursing home databases. The results show that it is possible to classify situations in elderly healthcare, and schedule resource planning according to expected health problems.
AB - It is believed that ICT-mediation for home care services increases patient empowerment, independency, self-efficacy and quality of life. Providing elderly people with tailored care services allows us to learn from patient data to predict future care needs. In this article, we demonstrate the contribution of machine learning to homecare services, using data collected by a home care services platform. As an actual case, we show how simulated medication compliance can be measured and modeled using clustering and regression techniques. The approach is validated using data from French nursing home databases. The results show that it is possible to classify situations in elderly healthcare, and schedule resource planning according to expected health problems.
KW - METIS-286328
KW - Decision support
KW - EWI-21771
KW - IR-80215
KW - Homecare
KW - Machine Learning
U2 - 10.1145/2189736.2189747
DO - 10.1145/2189736.2189747
M3 - Conference contribution
SN - 978-1-4503-1189-2
SP - 48
EP - 54
BT - Proceedings of the 4th International Workshop on Web Intelligence & Communities
PB - Association for Computing Machinery (ACM)
CY - New York
ER -