An architecture for complex clinical question answering

Authors:
Rodney D. Nielsen;James Masanz;Philip Ogren;Wayne Ward;James H. Martin;Guergana Savova;Martha Palmer
Affiliations:
University of Colorado, Boulder, CO, USA;Mayo Clinic, Rochester, MN, USA;University of Colorado, Boulder, CO, USA;University of Colorado, Boulder, CO, USA;University of Colorado, Boulder, CO, USA;Childrens Hospital Boston and Harvard Medical School, Boston, MA, USA;University of Colorado Boulder, Boulder, CO, USA
Venue:
Proceedings of the 1st ACM International Health Informatics Symposium
Year:
2010

Citing 7
Cited 3

Leveraging a common representation for personalized search and summarization in a medical digital library

Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
UIMA: an architectural approach to unstructured information processing in the corporate research environment

Natural Language Engineering
The Proposition Bank: An Annotated Corpus of Semantic Roles

Computational Linguistics
Learning question classifiers: the role of semantic information

Natural Language Engineering
Semantic role labeling using different syntactic views

ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Situated question answering in the clinical domain: selecting the best drug treatment for diseases

SumQA '06 Proceedings of the Workshop on Task-Focused Summarization and Question Answering
Biomedical question answering: A survey

Computer Methods and Programs in Biomedicine

An ontology for clinical questions about the contents of patient notes

Journal of Biomedical Informatics
Anaphoric reference in clinical reports: Characteristics of an annotated corpus

Journal of Biomedical Informatics
Fast and robust part-of-speech tagging using dynamic model selection

ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present the software architecture for a coming community resource, the Multi-source Integrated Platform for Answering Clinical Questions (MiPACQ). This system is designed to capitalize on state-of-the-art semantic annotation of text to answer complex clinical practice questions and to enable clinical investigators to perform pioneering data mining tasks. The architecture allows easy customization to facilitate integration with different electronic medical records systems and data sources, to retrain machine learning (ML) classifiers to handle domain-specific details, to utilize new annotators and ML algorithms as they become available, and to enhance, replace or add new core system components.