Electronic Health Records (EHR) Extraction Using Various NLP Models
Creators
- 1. Department of Computer Science and Engineering, VIT University, Vellore (Tamil Nadu), India.
Description
Abstract: Information recorded in electronic medical health records, clinical reports, and summaries has the possibility of revolutionizing health-related research and its corresponding industry. EMR data can be used for epidemiological studies, disease registries, data banks, drug safety surveillance, clinical trials, and healthcare audits. With the rapid adoption of electronic health records (EHRs), it is desirable to harvest information and knowledge from EHRs to support automated systems and to enable secondary use of EHRs for clinical and translational research; thereby increasing efficiency. One critical component which is predominantly used to facilitate the secondary use of EHR data is information extraction (IE) task, which automatically extracts and encodes clinical information from a given text. Now, a natural language processing model (NLP) focuses on “developing computational models for understanding the interaction between data science and language”. In the clinical domain, researchers have often used NLP systems to identify clinical syndromes and common biomedical concepts from imaging data, radiology reports, discharge summaries, problem lists, nursing documentation, drug reviews, and medical education documents. These data can help doctors determine patients' health condition(s) including diagnostic information, procedures and tests performed, treatment results, drugs administered, and more. Therefore, we hope to gain some insights and develop strategies to improve the utilization of these NLP systems in the clinical domain. We hope to provide a vision for addressing the existing data challenge(s) in this domain. For this, we would look at the various models that have been used/published over the years and test them for their attributes including effectiveness, accuracy, precision, etc. We believe that adding a probabilistic graphical model framework for structured output prediction would further improve the performance of our system. This experiment remains our future work.
Files
C3008061321.pdf
Files
(447.5 kB)
Name | Size | Download all |
---|---|---|
md5:5b565f0c6996c42ef0787881f59b17ec
|
447.5 kB | Preview Download |
Additional details
Identifiers
- DOI
- 10.54105/ijamst.C3008.05011224
- EISSN
- 2582-7596
Dates
- Accepted
-
2024-12-15Manuscript received on 14 March 2024 | Revised Manuscript received on 25 October 2024 | Manuscript Accepted on 15 December 2024 | Manuscript published on 30 December 2024.
References
- Baer B, Nguyen M, Woo EJ, Winiecki S, Scott J, Martin D, Botsis T, Ball R. Can Natural Language Processing Improve the Efficiency of Vaccine Adverse Event Report Review? Methods Inf Med. 2016;55(2):144-50. Epub 2015 Sep 23. PMID: 26394725. doi. https://doi.org/10.3414/ME14-01-0066
- Ruud KL, Johnson MG, Liesinger JT, Grafft CA, Naessens JM. Automated detection of follow-up appointments using text mining of discharge records. Int J Qual Health Care. 2010 Jun;22(3):229-35. Epub 2010 Mar 27. PMID: 20348557. doi. https://doi.org/10.1093/intqhc/mzq012
- Rochefort, C. & Verma, Aman & Eguale, T. & Buckeridge, David. (2015). O-037: Surveillance of adverse events in elderly patients: A study on the accuracy of applying natural language processing techniques to electronic health record data. European Geriatric Medicine. 6. S15. doi. https://doi.org/10.1016/S1878-7649(15)30050-4
- Kalra, Dipak & Singleton, Peter & Milan, J & Mackay, J & Detmer, D & Rector, Alan & Ingram, David. (2005). Security and confidentiality approach for the Clinical E-Science Framework (CLEF). Methods of information in medicine. 44. 193-7. 10.1267/METH05020193. doi. https://doi.org/10.1055/s-0038-1633945
- St-Maurice J, Kuo MH, Gooch P. A proof of concept for assessing emergency room use with primary care data and natural language processing. Methods Inf Med. 2013;52(1):33-42. Epub 2012 Dec 7. PMID: 23223678. doi. https://doi.org/10.3414/ME12-01-0012
- Khare R, Li J, Lu Z. LabeledIn: cataloging labeled indications for human drugs. J Biomed Inform. 2014 Dec; 52:448-56. Epub 2014 Aug 23. PMID: 25220766; PMCID: PMC4260997. doi. https://doi.org/10.1016/j.jbi.2014.08.004
- Shashi, Dr. M. (2022). Leveraging Blockchain-Based Electronic Health Record Systems in Healthcare 4.0. In International Journal of Innovative Technology and Exploring Engineering (Vol. 12, Issue 1, pp. 1–5). doi. https://doi.org/10.35940/ijitee.a9359.1212122
- Patel, I., Jain, S., Vishwajeet, J. K., Aggarwal, V., & Mehra, P. (2021). Securing Electronic Healthcare Records in Web Applications. In International Journal of Engineering and Advanced Technology (Vol. 10, Issue 5, pp. 236–242). doi. https://doi.org/10.35940/ijeat.e2781.0610521
- Hussain, Dr. M. K., Hussain, M. J., Bakri, M., Abdurraheem, T. M., & Al-Areefi, M. (2020). Big Data in Healthcare. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 6, pp. 2127– 2131). doi. https://doi.org/10.35940/ijrte.f8100.038620
- Khan, N. D., Younas, M., Khan, M. T., Duaa, & Zaman, A. (2021). The Role of Big Data Analyticsin Healthcare. In International Journal of Soft Computing and Engineering (Vol. 11, Issue 1, pp. 1–7). doi. https://doi.org/10.35940/ijsce.a3523.0911121
- . Jeyaraj, B. Dr. P., & Narayanan AVSM, L. G. T. (2023). Role of Artificial Intelligence in Enhancing Healthcare Delivery. In International Journal of Innovative Science and Modern Engineering (Vol. 11, Issue 12, pp. 1–13). doi. https://doi.org/10.35940/ijisme.a1310.12111223