Paper Info
Title
Medical Workflow Modeling Using Alignment-Guided State-Splitting HMM
Abstract
Process mining techniques have been used to discover and analyze workflows in various fields, ranging from business management to healthcare. Much of this research, however, has overlooked the potential of hidden Markov models (HMMs) for workflow discovery. We present a novel alignment-guided state-splitting HMM inference algorithm (AGSS) for discovering workflow models based on observed traces of process executions. We compared the AGSS to existing methods using four real-world medical workflow datasets and a more detailed case study on one of them. Our numerical results show that AGSS not only generates more accurate workflow models, but also better represents the underlying process. In addition, with trace alignment to guide state splitting, AGSS is significantly more efficient (by a factor of O(n)) than previous HMM inference algorithms. Our case study results show that our approach produces a more readable and accurate workflow model that existing algorithms. Comparing the discovered model to the hand-made expert model of the same process, we found three discrepancies. These three discrepancies were reconsidered by medical experts and used for enhancing the expert model.
Year
DOI
Venue
2017
10.1109/ICHI.2017.66
2017 IEEE International Conference on Healthcare Informatics (ICHI)
Keywords
Field
DocType
Process Mining,Medical Workflow,Hidden Markov Model,State-splitting Algorithm,Trace Alignment
Data mining,Computer science,Inference,Workflow modeling,Ranging,Business management,Artificial intelligence,Hidden Markov model,Workflow model,Workflow,Machine learning,Process mining
Conference
Volume
ISBN
Citations 
2017
978-1-5090-4882-3
2
PageRank 
References 
Authors
0.38
10
7
Name
Order
Citations
PageRank
Sen Yang1103.98
Moliang Zhou2163.55
Shuhong Chen34910.21
Xin Dong4113.97
Omar Ahmed520.38
Randall S. Burd612221.53
Ivan Marsic771691.96