Assessing the similarity of surface linguistic features related to epilepsy across pediatric hospitals
Open accessAbstract
OBJECTIVE: The constant progress in computational linguistic methods provides amazing opportunities for discovering information in clinical text and enables the clinical scientist to explore novel approaches to care. However, these new approaches need evaluation. We describe an automated system to compare descriptions of epilepsy patients at three different organizations: Cincinnati Children's Hospital, the Children's Hospital Colorado, and the Children's Hospital of Philadelphia. To our knowledge, there have been no similar previous studies. MATERIALS AND METHODS: In this work, a support vector machine (SVM)-based natural language processing (NLP) algorithm is trained to classify epilepsy progress notes as belonging to a patient with a specific type of epilepsy from a particular hospital. The same SVM is then used to classify notes from another hospital. Our null hypothesis is that an NLP algorithm cannot be trained using epilepsy-specific notes from one hospital and subsequently used to classify notes from another hospital better than a random baseline classifier. The hypothesis is tested using epilepsy progress notes from the three hospitals. RESULTS: We are able to reject the null hypothesis at the 95% level. It is also found that classification was improved by including notes from a second hospital in the SVM training sample. DISCUSSION AND CONCLUSION: With a reasonably uniform epilepsy vocabulary and an NLP-based algorithm able to use this uniformity to classify epilepsy progress notes across different hospitals, we can pursue automated comparisons of patient conditions, treatments, and diagnoses across different healthcare settings.
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Connolly et al. [169]'s NLP algorithm successfully classified partial epilepsy, generalized epilepsy, and unclassified epilepsy with remarkable accuracy.
“By analyzing text features extracted from electronic medical records, their algorithm successfully classified different subtypes of epilepsy with remarkable accuracy.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [169]
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Incidence of Epilepsy and Unprovoked Seizures in Rochester, Minnesota: 1935–1984
Hauser, W. A., Annegers, J. F., & Kurland, L. T. (1993) · Epilepsia · doi:10.1111/j.1528-1157.1993.tb02586.x · Cited by 2,084 · see citing works
Global disparities in the epilepsy treatment gap: a systematic review
Meyer, A.-C. L., Dua, T., Ma, J., Saxena, S., & Birbeck, G. L. (2009) · Bulletin of the World Health Organization · doi:10.2471/blt.09.064147 · Cited by 499 · see citing works
Quality improvement in neurology: AAN epilepsy quality measures
Fountain, N. B., Ness, P. C. V., Swain‐Eng, R. J., Tonn, S. T., & Bever, C. T. (2010) · Neurology · doi:10.1212/wnl.0b013e318203e9d1 · Cited by 227 · see citing works
Berg, A. T., Shinnar, S., Levy, S. R., Testa, F. M., Smith‐Rapaport, S., & Beckerman, B. (2000) · Epilepsia · doi:10.1111/j.1528-1157.2000.tb04604.x · Cited by 154 · see citing works
The MITRE Identification Scrubber Toolkit: Design, training, and assessment
Aberdeen, J. S., Bayer, S., Yeniterzi, R., Wellner, B., Clark, C., Hanauer, D. A., Malin, B., & Hirschman, L. (2010) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2010.09.007 · Cited by 146 · see citing works
Machine Learning and Rule-based Approaches to Assertion Classification
Uzuner, Ö., Zhang, X., & Sibanda, T. (2008) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m2950 · Cited by 98 · see citing works
McClelland, S., Guo, H., & Okuyemi, K. S. (2010) · Archives of Neurology · doi:10.1001/archneurol.2010.86 · Cited by 86 · see citing works
Ivanova, J. I., Birnbaum, H. G., Kidolezi, Y., Qiu, Y., Mallett, D. A., & Caleo, S. (2009) · Epilepsia · doi:10.1111/j.1528-1167.2009.02422.x · Cited by 54 · see citing works
Part-of-speech tagging for clinical text: wall or bridge between institutions?
Fan, J.-W., Prasad, R., Yabut, R. M., et al. (2011) · PubMed · Cited by 33 · see citing works
Effect of small sample size on text categorization with support vector machines
Matykiewicz, P., & Pestian, J. P. (2012) · North American Chapter of the Association for Computational Linguistics · Cited by 23 · see citing works
Self-citationEarlier Identification of Epilepsy Surgery Candidates Using Natural Language Processing
Matykiewicz, P., Cohen, K. B., Holland, K. D., et al. (2013) · Cited by 10 · see citing works
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A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
Xu, X., Li, J., Zhu, Z., Zhao, L., Wang, H., Song, C., Chen, Y., Zhao, Q., Yang, J., & Pei, Y. (2024) · Bioengineering · doi:10.3390/bioengineering11030219 · Cited by 242 · see citing works
- Authors
- Connolly, B., Matykiewicz, P., Bretonnel Cohen, K., Standridge, S. M., Glauser, T. A., Dlugos, D. J., Koh, S., Tham, E., & Pestian, J.
- Affiliations
- Cincinnati Children's Hospital Medical Center (US)
- Children's Hospital of Philadelphia (US)
- Children's Hospital Colorado (US)
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-09-01
- Volume
- 21
- Issue
- 5
- Pages
- 866-870
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC4147613
- OpenAlex
- W2106636419
- Cited by
- 26
- References
- 11
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 4 · 2022: 2 · 2021: 4 · 2019: 3 · 2018: 3 · 2016: 3 · 2015: 1
- FWCI
- 1.33
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- cc-by-nc
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (1r01lm011124-01, 1R01LM011124)
- U.S. National Library of Medicine (1R01LM011124, R01-LM011124)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- epilepsy, pediatric epilepsy, clinical progress notes, natural language processing, support vector machine, clinical text classification, clinical text mining
- MeSH terms
- Epilepsy, Hospitals, Pediatric, Humans, Linguistics, Natural Language Processing, Terminology as Topic, Electronic Health Records, Support Vector Machine
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