Name
Title | ||
|---|---|---|
An efficient pattern mining approach for event detection in multivariate temporal data |
Abstract | ||
|---|---|---|
This work proposes a pattern mining approach to learn event detection models from complex multivariate temporal data, such as electronic health records. We present recent temporal pattern mining, a novel approach for efficiently finding predictive patterns for event detection problems. This approach first converts the time series data into time-interval sequences of temporal abstractions. It then constructs more complex time-interval patterns backward in time using temporal operators. We also present the minimal predictive recent temporal patterns framework for selecting a small set of predictive and non-spurious patterns. We apply our methods for predicting adverse medical events in real-world clinical data. The results demonstrate the benefits of our methods in learning accurate event detection models, which is a key step for developing intelligent patient monitoring and decision support systems. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s10115-015-0819-6 | Knowledge and Information Systems |
Keywords | Field | DocType |
Temporal data mining, Electronic health records, Temporal abstractions, Time-interval patterns, Recent temporal patterns, Event detection | Data mining,Time series,Text mining,Computer science,Remote patient monitoring,Multivariate statistics,Decision support system,Temporal database,Artificial intelligence,Operator (computer programming),Small set,Machine learning | Journal |
Volume | Issue | ISSN |
46 | 1 | 0219-3116 |
Citations | PageRank | References |
14 | 0.56 | 41 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Iyad Batal | 1 | 192 | 10.61 |
| Gregory F. Cooper | 2 | 3464 | 580.16 |
| Dmitriy Fradkin | 3 | 344 | 19.25 |
| james h harrison | 4 | 14 | 0.56 |
| Fabian Moerchen | 5 | 172 | 10.51 |
| Milos Hauskrecht | 6 | 921 | 90.70 |