Intention mining in medical process: A case study in trauma resuscitation

Document Type

Conference Proceeding

Publication Date

7-24-2018

Journal

Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018

DOI

10.1109/ICHI.2018.00012

Keywords

Hierarchical Hidden Markov Model; Intention Mining; Process Mining; Trauma Resuscitation

Abstract

© 2018 IEEE. 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.

This document is currently not available here.

Share

COinS