Template:Article of the week
Extracting structured data from narrated medical reports is challenged by the complexity of heterogeneous structures and vocabularies and often requires significant manual effort. Traditional machine-based approaches lack the capability to take user feedback for improving the extraction algorithm in real time.
Our goal was to provide a generic information extraction framework that can support diverse clinical reports and enables a dynamic interaction between a human and a machine that produces highly accurate results.
A clinical information extraction system IDEAL-X has been built on top of online machine learning. It processes one document at a time, and user interactions are recorded as feedback to update the learning model in real time. The updated model is used to predict values for extraction in subsequent documents. Once prediction accuracy reaches a user-acceptable threshold, the remaining documents may be batch processed. A customizable controlled vocabulary may be used to support extraction. (Full article...)
Recently featured:
- ▪ Selecting a laboratory information management system for biorepositories in low- and middle-income countries: The H3Africa experience and lessons learned
- ▪ Baobab Laboratory Information Management System: Development of an open-source laboratory information management system for biobanking
- ▪ The FAIR Guiding Principles for scientific data management and stewardship
