Evaluation of lexicon- and syntax-based negation detection algorithms using clinical text data
-
J. Manimaran
und
T. Velmurugan
Abstract
Background:
Clinical Text Analysis and Knowledge Extraction System (cTAKES) is an open-source natural language processing (NLP) system. In recent development modules of cTAKES, a negation detection (ND) algorithm is used to improve annotation capabilities and simplify automatic identification of negative context in large clinical documents. In this research, the two types of ND algorithms used are lexicon and syntax, which are analyzed using a database made openly available by the National Center for Biomedical Computing. The aim of this analysis is to find the pros and cons of these algorithms.
Methods:
Patient medical reports were collected from three institutions included the 2010 i2b2/VA Clinical NLP Challenge, which is the input data for this analysis. This database includes patient discharge summaries and progress notes. The patient data is fed into five ND algorithms: NegEx, ConText, pyConTextNLP, DEEPEN and Negation Resolution (NR). NegEx, ConText and pyConTextNLP are lexicon-based, whereas DEEPEN and NR are syntax-based. The results from these five ND algorithms are post-processed and compared with the annotated data. Finally, the performance of these ND algorithms is evaluated by computing standard measures including F-measure, kappa statistics and ROC, among others, as well as the execution time of each algorithm.
Results:
This research is tested through practical implementation based on the accuracy of each algorithm’s results and computational time to evaluate its performance in order to find a robust and reliable ND algorithm.
Conclusions:
The performance of the chosen ND algorithms is analyzed based on the results produced by this research approach. The time and accuracy of each algorithm are calculated and compared to suggest the best method.
Acknowledgments
We thank S. Shahul Hameed and Gokulalakshmi Elayaperumal from Sree Balaji Medical College and Hospital, Chennai, India, George Gkotsis from IoPPN, King’s College London, and Saeed Mehrabi from the School of Informatics and Computing, Indiana University, Indianapolis, IN, USA, who offered their continuous support in the implementation of this task.
Author contributions: The authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
Appendix A
Example of single annotated i2b2 report.
| Sentences (report file) | Chest CT scan was negative for pulmonary embolism but positive for consolidation |
|---|---|
| Assertion annotation | c=“pulmonary embolism” 21:6 21:7 | | t= “problem” | | a=“absent” |
| (Annotated file) | c=“consolidation” 21:11 21:11 | | t=“problem” | | a=“present” |
A sample input data.
| Concepts | Sentences |
|---|---|
| Mass | No mass or vegetation is seen on the mitral valve |
| Pericardial effusion | There is no pericardial effusion |
| Epileptiform features | No epileptiform features were seen |
| Infection | CXR, LP, UA and abdominal CT showed no sign of infection |
| Orthostatic | She was not orthostatic |
| A headache | He did not complain about a headache |
Evaluation of the algorithm’s output using 2*2 table.
| Predicted output | ||
|---|---|---|
| True (negated) | False (affirmed) | |
| Manual annotations | ||
| True (negated) | True positive (TP) | False negative (FN) |
| False (affirmed) | False positive (FP) | True negative (TN) |
A simple surface-based approach of ConText algorithm.
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©2017 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Mechanism of ligand binding – PDZ domain taken as example
- Chain-chain complexation and heme binding in haemoglobin with respect to the hydrophobic core structure
- Modified S-transform as a tool to identify secondary structure elements in RNA
- Electronic health record for elderly patients
- Evaluation of lexicon- and syntax-based negation detection algorithms using clinical text data
- Short Communication
- An improved structural model of the human iron exporter ferroportin. Insight into the role of pathogenic mutations in hereditary hemochromatosis type 4
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Mechanism of ligand binding – PDZ domain taken as example
- Chain-chain complexation and heme binding in haemoglobin with respect to the hydrophobic core structure
- Modified S-transform as a tool to identify secondary structure elements in RNA
- Electronic health record for elderly patients
- Evaluation of lexicon- and syntax-based negation detection algorithms using clinical text data
- Short Communication
- An improved structural model of the human iron exporter ferroportin. Insight into the role of pathogenic mutations in hereditary hemochromatosis type 4
