共 2 条
Using Machine Learning on mHealth-based Data Sources
被引:0
作者:
Pryss, Rudiger
[1
]
Schickler, Marc
[2
]
Schobel, Johannes
[3
]
Schlee, Winfried
[4
]
Spiliopoulou, Myra
[5
]
Probst, Thomas
[6
]
Beierle, Felix
[1
]
机构:
[1] Univ Wurzburg, Inst Clin Epidemiol & Biometry, D-97080 Wurzburg, Germany
[2] Ulm Univ, Inst Databases & Informat Syst, D-89081 Ulm, Germany
[3] Neu Ulm Univ Appl Sci, Inst DigiHlth, D-89231 Neu Ulm, Germany
[4] Univ Regensburg, Dept Psychiat & Psychotherapy, D-93053 Regensburg, Germany
[5] Otto Von Guericke Univ, Knowledge Management & Discovery Lab, D-39106 Magdeburg, Germany
[6] Danube Univ Krems, Dept Psychotherapy & Biopsychosocial Hlth, Krems An Der Donau, Austria
来源:
ARTIFICIAL INTELLIGENCE IN MEDICINE, AIME 2022
|
2022年
/
13263卷
关键词:
mHealth;
Machine learning;
Data collection strategies;
D O I:
10.1007/978-3-031-09342-5
中图分类号:
TP18 [人工智能理论];
学科分类号:
081104 ;
0812 ;
0835 ;
1405 ;
摘要:
The application of machine learning algorithms has become important for the medical domain. However, the concrete application of these type of algorithms strongly depends on how a corresponding data source was created. Most importantly, domain knowledge must be linked with data science knowledge. Data collected using smartphones or smart mobile devices (e.g., smart watches) is commonly referred to as mHealth data. The possibilities and strategies for collecting data in this area now appear to be as diverse as the machine learning algorithms that have emerged. This tutorial will therefore discuss how mHealth data is structured and which aspects need to be taken into account when evaluating it with machine learning algorithms, using concrete examples.
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页码:443 / 445
页数:3
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