Name
Abstract | ||
|---|---|---|
In medical processes such as surgical procedures and trauma resuscitations, medical teams perform treatment activities according to underlying invisible goals or intentions. In this study, we presented an approach to uncover these intentions from observed treatment activities. Developed on top of a hierarchical hidden Markov model (H-HMM), our approach can identify multi-level intentions. To accurately infer the H-HMM, we used state splitting method with maximum a posteriori probability (MAP) as the scoring function. We evaluated our approach in both qualitative and quantitative ways, using a case study of the trauma resuscitation process. This dataset includes 123 trauma resuscitation cases collected at a level 1 trauma center. Our results show our intention mining achieved an accuracy of 86.6% in classifying medical teams' intentions. This work shows the feasibility of unsupervised intention mining of complex real-world medical processes. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICHI.2018.00012 | 2018 IEEE International Conference on Healthcare Informatics (ICHI) |
Keywords | Field | DocType |
Intention Mining,Process Mining,Trauma Resuscitation,Hierarchical Hidden Markov Model | Hierarchical hidden Markov model,Computer science,Trauma resuscitation,Probability distribution,Artificial intelligence,Trauma center,Maximum a posteriori estimation,Hidden Markov model,Machine learning | Conference |
Volume | ISBN | Citations |
2018 | 978-1-5386-5378-4 | 0 |
PageRank | References | Authors |
0.34 | 3 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sen Yang | 1 | 10 | 3.98 |
| Weiqing Ni | 2 | 0 | 0.34 |
| Xin Dong | 3 | 11 | 3.97 |
| Shuhong Chen | 4 | 49 | 10.21 |
| Richard A. Farneth | 5 | 11 | 4.44 |
| Aleksandra Sarcevic | 6 | 182 | 26.75 |
| Ivan Marsic | 7 | 716 | 91.96 |
| Randall S. Burd | 8 | 122 | 21.53 |