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Achievability to Extract Specific Date Information for Cancer Research
Liwei Wang1, Jason Wampfler1, Angela Dispenzieri1
1Mayo Clinic, Rochester, MN, USA.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|April 21, 2020
Summary
Extracting precise dates from cancer research is vital but challenging for current natural language processing (NLP) systems. This study explores NLP
Area of Science:
- Oncology
- Bioinformatics
- Natural Language Processing
Background:
- Accurate temporal information, specifically dates, is critical for advancing cancer research.
- Existing natural language processing (NLP) systems face challenges in extracting precise date information relevant to cancer events.
Purpose of the Study:
- To investigate the feasibility of date information extraction for cancer research using NLP.
- To evaluate the performance and discuss the challenges of NLP-based date extraction in this domain.
Main Methods:
- Utilized two case studies to illustrate the process.
- Applied and evaluated NLP techniques for identifying and extracting date information from cancer research data.
Main Results:
- Demonstrated the feasibility of extracting date information for cancer research.
- Identified specific performance metrics and highlighted key challenges in the extraction process.
Conclusions:
- Date information extraction for cancer research is feasible but requires specialized NLP approaches.
- Further development is needed to overcome identified challenges and improve accuracy in temporal data extraction for oncology studies.
