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Identifying Patients' Smoking Status from Electronic Dental Records Data
Jay Patel1, Zasim Siddiqui1, Anand Krishnan1
1Indiana University School of Dentistry, Indianapolis, IN, USA.
Studies in Health Technology and Informatics
|January 4, 2018
Summary
This study developed a natural language processing (NLP) system to automatically extract patient smoking status from dental records. This improves access to crucial smoking data for clinical care and research.
Area of Science:
- Oral Health
- Biomedical Informatics
- Natural Language Processing
Background:
- Smoking is a major risk factor for oral diseases.
- Accurate patient smoking status is vital for clinical decisions and treatment.
- Current methods for accessing smoking data from electronic dental records are limited.
Purpose of the Study:
- To develop and evaluate a natural language processing (NLP) system.
- To automatically extract patient smoking status from free-text electronic dental records.
- To overcome accessibility obstacles for clinical care and research utility.
Main Methods:
- Development of a novel NLP system tailored for dental records.
- Evaluation of the NLP system's performance in extracting smoking status.
- Utilizing free-text clinical notes within the Electronic Dental Record.
Main Results:
- The study successfully developed and evaluated an NLP system.
- The system demonstrates the potential for automated smoking status extraction.
- This addresses a gap in current research and clinical practice.
Conclusions:
- Automated extraction of smoking status from Electronic Dental Records is feasible.
- The developed NLP system can enhance clinical decision-making.
- Improved data accessibility supports oral disease research and patient care.