Name
Title | ||
|---|---|---|
An integrated approach for healthcare planning over multi-dimensional data using long-term prediction |
Abstract | ||
|---|---|---|
The mining of temporal aspects over multi-dimensional data is increasingly critical for healthcare planning tasks. A healthcare planning task is, in essence, a classification problem over health-related attributes across temporal horizons. The increasingly integration of healthcare data through multi-dimensional structures triggers new opportunities for an adequate long-term planning of resources within and among clinical, pharmaceutical, laboratorial, insurance and e-health providers. However, the flexible nature and random occurrence of health records claim for the ability to deal with both structural attribute-multiplicity and arbitrarily-high temporal sparsity. For this purpose, two solutions using different structural mappings are proposed: an adapted multi-label classifier over denormalized tabular data and an adapted multiple time-point classifier over multivariate sparse time sequences. This work motivates the problem of long-term prediction in healthcare, and places key requirements and principles for its accurate and efficient solution. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-29361-0_6 | HIS |
Keywords | Field | DocType |
arbitrarily-high temporal sparsity,integrated approach,adequate long-term planning,temporal horizon,healthcare data,denormalized tabular data,classification problem,multi-dimensional data,healthcare planning task,long-term prediction,different structural mapping,temporal aspect | Inductive logic programming,Health care,Multi dimensional data,Data mining,Time series,Long-term prediction,Multivariate statistics,Computer science,Artificial intelligence,Classifier (linguistics),Machine learning | Conference |
Citations | PageRank | References |
1 | 0.37 | 29 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Henriques | 1 | 22 | 4.20 |
| Cláudia Antunes | 2 | 161 | 16.57 |