Experimenting with Generative Adversarial Networks to Expand Sparse Physiological Time-Series Data.
Stud Health Technol Inform
; 271: 248-255, 2020 Jun 23.
Article
en En
| MEDLINE
| ID: mdl-32578570
ABSTRACT
Machine Learning research and its application have gained enormous relevance in recent years. Their usage in medical settings could support patients, increase patient safety and assist health professionals in various tasks. However, medical data is often sparse, which renders big data analytics methods like deep learning ineffective. Data synthesis helps to augment small data sets and potentially improves patient data integrity. The presented work illustrates how Generative Adversarial Networks can be applied specifically to small data sets for enlarging sparse data. Following a state-of-the-art analysis is conducted, experimental methods with such networks are documented, which have been applied to three different data sets. Results from all three sets are presented and take-away messages are summarized. Concluding, the results' quality and limitations of the work are discussed.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Aprendizaje Automático
Tipo de estudio:
Risk_factors_studies
Idioma:
En
Revista:
Stud Health Technol Inform
Asunto de la revista:
INFORMATICA MEDICA
/
PESQUISA EM SERVICOS DE SAUDE
Año:
2020
Tipo del documento:
Article
País de afiliación:
Austria