Journal article
Intention Mining in Medical Process: A Case Study in Trauma Resuscitation
IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics, v 2018, pp 36-43
Jun 2018
PMID: 30443647
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- Intention Mining in Medical Process: A Case Study in Trauma Resuscitation
- Creators
- Sen Yang - Electrical and Computer Engineering Department Rutgers University Piscataway, NJ 08854, USAWeiqing Ni - Electrical and Computer Engineering Department Rutgers University Piscataway, NJ 08854, USAXin Dong - Electrical and Computer Engineering Department Rutgers University Piscataway, NJ 08854, USAShuhong Chen - Electrical and Computer Engineering Department Rutgers University Piscataway, NJ 08854, USARichard A Farneth - Division of Trauma and Burn Surgery, Children’s Nat’l Medical Center Washington, DC 20010, USAAleksandra Sarcevic - College of Computing and Informatics Drexel University Philadelphia, PA 19104, USAIvan Marsic - Electrical and Computer Engineering Department Rutgers University Piscataway, NJ 08854, USARandall S Burd - Division of Trauma and Burn Surgery, Children’s Nat’l Medical Center Washington, DC 20010, USA
- Publication Details
- IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics, v 2018, pp 36-43
- Publisher
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000853207500005
- Scopus ID
- 2-s2.0-85051138263
- Other Identifier
- 153865377X; 9781538653777; 991014976882504721
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InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Health Care Sciences & Services
- Medical Informatics