Computer-assisted medical billing information extraction: comparing rule-based and end-to-end transfer learning approaches
- Resource Type
- Conference
- Authors
- Chen, Suhao; Le, Tuan-Dung; Thieu, Thanh; Miao, Zhuqi; Nguyen, Phuong D.; Gin, Andrew
- Source
- 2021 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) CHASE Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2021 IEEE/ACM Conference on. :132-133 Dec, 2021
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Robotics and Control Systems
Transfer learning
Pipelines
Neural networks
Medical services
Information retrieval
Natural language processing
Filling
medical billing
natural language processing
information extraction
rule-based
deep learning
- Language
Medical billing is important for both healthcare providers and payers, yet filling reimbursement requires tremen-dous effort to process clinical notes, making it labor-intensive and error-prone. This work compares two natural language processing (NLP) approaches to extract patients' history information from clinical notes for billing purposes. A rule-based pipeline built on top of a generic clinical NLP tool CLAMP, is compared against an end-to-end deep neural network architecture. We annotate a gold-standard corpus to evaluate the two approaches. Results show information extraction for medical billing is a challenging problem though NLP has great potential to automate the task. Our work is the first academic study using NLP in Evaluation and Management billing.