Name
Abstract | ||
|---|---|---|
Osteoporosis is a disease of bones that results in an increased risk of bone fracture. The diagnosis of Osteoporosis is usually performed by measuring the Bone Mineral Density (BMD) using Dual-Energy X-ray Absorptiometry (DEXA) scanning. In this work, we introduce the use of Venn Prediction in order to assess the risk of Osteoporosis before a DEXA scan, based on known risk factors. Unlike other probabilistic methods, Venn Predictors can provide well-calibrated probabilistic outputs under the assumption that the data used are identically and independently distributed (i.i.d.). Our contribution is two-fold: Firstly, we have collected real-world data from various clinic centres in Cyprus which based on their locality can be used for analysis of Osteoporosis risk factors specifically for Cypriot patients. To the best of our knowledge, local data in Cyprus for Osteoporosis risk assessment have not been collected before. Secondary, our results demonstrate that our method can provide probabilistic outputs that may be practical and trustful to physicians. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-41142-7_44 | ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2013 |
Keywords | DocType | Volume |
well calibrated probabilities, osteoporosis, risk assessment, Venn Predictor, Machine Learning | Conference | 412 |
ISSN | Citations | PageRank |
1868-4238 | 2 | 0.36 |
References | Authors | |
17 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonis Lambrou | 1 | 58 | 4.41 |
| Harris Papadopoulos | 2 | 219 | 26.33 |
| Alexander Gammerman | 3 | 809 | 109.82 |