Name
Title | ||
|---|---|---|
Importance of medical data preprocessing in predictive modeling and risk factor discovery for the frailty syndrome. |
Abstract | ||
|---|---|---|
This work demonstrates that it is feasible to use incomplete, imbalanced medical data for the development of a predictive model for the frailty syndrome. Moreover, potential predictive factors have been discovered, which were clinically approved by the clinicians. Future work will improve prediction accuracy, especially with regard to separating the group of frail patients into frail and pre-frail ones and analyze the differences among them. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1186/s12911-019-0747-6 | BMC medical informatics and decision making |
Keywords | Field | DocType |
Data mining,Data preprocessing,Frailty syndrome,Health data analytics,Machine learning,Missing value imputation,Predictive modeling,Risk factor discovery | Frailty syndrome,Knowledge management,Adverse effect,Data pre-processing,Intensive care medicine,Life expectancy,Clinical decision support system,Missing value imputation,Health informatics,Medicine,Risk factor | Journal |
Volume | Issue | ISSN |
19 | 1 | 1472-6947 |
Citations | PageRank | References |
1 | 0.37 | 11 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas Philipp Hassler | 1 | 1 | 0.37 |
| Ernestina Menasalvas | 2 | 228 | 26.91 |
| francisco garciagarcia | 3 | 1 | 0.71 |
| Leocadio Rodríguez-Mañas | 4 | 1 | 1.39 |
| Andreas Holzinger | 5 | 53 | 3.48 |