Name
Abstract | ||
|---|---|---|
Process mining techniques have been applied to the visualization, interpretation, and analysis of medical processes. However, only a very limited amount of process data necessary for these analyses is publicly available, especially in the medical field because of patientsu0027 privacy. This limits novel medical process research to using insufficiently large or randomly-generated synthetic datasets. Our goal in this study is to train a model (using a limited amount of observed process data) that can generate large amounts of semi-synthetic process data. This generated data has characteristics similar to those of real-world process data, and could potentially be observed in reality. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICHI.2017.67 | 2017 IEEE INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS (ICHI) |
Field | DocType | Volume |
Informatics,Data mining,Data generator,Visualization,Computer science,Trauma resuscitation,Artificial intelligence,Hidden Markov model,Machine learning,Process mining | Conference | 2017 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sen Yang | 1 | 10 | 3.98 |
| Yichen Zhou | 2 | 0 | 0.34 |
| Yifeng Guo | 3 | 55 | 4.93 |
| Richard A. Farneth | 4 | 11 | 4.44 |
| Ivan Marsic | 5 | 716 | 91.96 |
| Randall S. Burd | 6 | 122 | 21.53 |