Intention Mining in Medical Process: A Case Study in Trauma Resuscitation.

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 present 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 is an exploration of unsupervised intention mining of complex real-world medical processes.