Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 30 |
| Seitenumfang | 16 |
| Fachzeitschrift | ACM Transactions on Computing for Healthcare |
| Jahrgang | 3 |
| Ausgabenummer | 3 |
| Publikationsstatus | Veröffentlicht - 7 Apr. 2022 |
Abstract
Early and precise prognosis of dementia is a critical medical challenge. The design of an optimal computational model that addresses this issue, and at the same time explains the underlying mechanisms that lead to output decisions, is an ongoing challenge. In this study, we focus on assessing the risk of an individual converting to Dementia in the short (next year) and long (one to five years) term, given only a few early-stage observations. Our goal is to develop a machine learning model that could assist the prediction of dementia from regular clinical data. The results show that combining various machine learning techniques together can successfully define ways to identify the risks of developing dementia over the following five years with accuracies considerably above average rates. These findings suggest that accurately developed models can be considered as a promising tool to improve early dementia prognosis.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Medizin (insg.)
- Medizin (sonstige)
- Informatik (insg.)
- Information systems
- Ingenieurwesen (insg.)
- Biomedizintechnik
- Informatik (insg.)
- Angewandte Informatik
- Medizin (insg.)
- Gesundheitsinformatik
- Gesundheitsberufe (insg.)
- Gesundheits-Informationsmanagement
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in: ACM Transactions on Computing for Healthcare, Jahrgang 3, Nr. 3, 30, 07.04.2022.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Improving Early Prognosis of Dementia Using Machine Learning Methods
AU - Katsimpras, Georgios
AU - Aisopos, Fotis
AU - Garrard, Peter
AU - Vidal, Maria Esther
AU - Paliouras, Georgios
N1 - Funding Information: This article is supported by European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 727658, Project IASIS (Integration and analysis of heterogeneous big data for precision medicine and suggested treatments for different types of patients).
PY - 2022/4/7
Y1 - 2022/4/7
N2 - Early and precise prognosis of dementia is a critical medical challenge. The design of an optimal computational model that addresses this issue, and at the same time explains the underlying mechanisms that lead to output decisions, is an ongoing challenge. In this study, we focus on assessing the risk of an individual converting to Dementia in the short (next year) and long (one to five years) term, given only a few early-stage observations. Our goal is to develop a machine learning model that could assist the prediction of dementia from regular clinical data. The results show that combining various machine learning techniques together can successfully define ways to identify the risks of developing dementia over the following five years with accuracies considerably above average rates. These findings suggest that accurately developed models can be considered as a promising tool to improve early dementia prognosis.
AB - Early and precise prognosis of dementia is a critical medical challenge. The design of an optimal computational model that addresses this issue, and at the same time explains the underlying mechanisms that lead to output decisions, is an ongoing challenge. In this study, we focus on assessing the risk of an individual converting to Dementia in the short (next year) and long (one to five years) term, given only a few early-stage observations. Our goal is to develop a machine learning model that could assist the prediction of dementia from regular clinical data. The results show that combining various machine learning techniques together can successfully define ways to identify the risks of developing dementia over the following five years with accuracies considerably above average rates. These findings suggest that accurately developed models can be considered as a promising tool to improve early dementia prognosis.
KW - Dementia
KW - machine learning
KW - prognosis
UR - http://www.scopus.com/inward/record.url?scp=85140035449&partnerID=8YFLogxK
U2 - 10.1145/3502433
DO - 10.1145/3502433
M3 - Article
AN - SCOPUS:85140035449
VL - 3
JO - ACM Transactions on Computing for Healthcare
JF - ACM Transactions on Computing for Healthcare
SN - 2691-1957
IS - 3
M1 - 30
ER -