Different data formats are challenging for the standardization and exchange of medical data. In addition, most medical data in medical information systems (MIS) or databases is stored in an unstructured way, causing difficulties in processing the data. The article proposes an approach for categorizing unstructured medical records of patients with allergies into the categories provided in the FHIR exchange standard. We developed a two-stage classification model based on manually labelled medical records. The method is based on machine learning algorithms, as well as international standards for the exchange of medical data. The model has shown high accuracy. The development of the presented approach for structuring medical texts will ensure the reuse and interoperability of medical data.
References
1. Douglas HE, et al. Implementing information and communication technology to support community aged care service integration: Lessons from an Australian aged care provider. J. Integr. Care. Igitur , Utrecht Publishing and Archiving Services. 2017; 17(1).
2. Fung KW, et al. Using SNOMED CT-encoded problems to improve ICD-10-CM coding—A randomized controlled experiment. J. Med. Inform. Elsevier Ireland Ltd. 2019; 126: 19–25.
3. Fiebeck J, et al. Implementing LOINC: Current status and ongoing work at the Hannover Medical School. Studies in Health Technology and Informatics. IOS Press. 2019; 258: 247–248.
4. Mascia C, et al. OpenEHR modeling for genomics in clinical practice. J. Med. Inform. Elsevier Ireland Ltd. 2018; 120: 147–156.
5. Santos MR, Bax MP, Kalra D. Building a logical EHR architecture based on ISO 13606 standard and semantic web technologies. Studies in Health Technology and Informatics. IOS Press. 2010; 160(1): 161–165.
6. Ulrich H, et al. Metadata repository for improved data sharing and reuse based on HL7 FHIR. Studies in Health Technology and Informatics. IOS Press. 2017; 228: 162–166.
7. Huff S.M, et al. Integrating detailed clinical models into application development tools. Stud. Health Technol. Inform. IOS Press. 2004; 107: 1058–1062.
8. Hong N, et al. Standardizing Heterogeneous Annotation Corpora Using HL7 FHIR for Facilitating their Reuse and Integration in Clinical NLP.AMIA. Annu. Symp. proceedings. 2018; 2018: 574–583.
9. Lenivtceva ID, Kopanitsa G. Evaluating Manual Mappings of Russian Proprietary Formats and Terminologies to FHIR. Methods Inf. Med. 2019; 58: 4–5.
10. Wang Y, et al. Clinical information extraction applications: A literature review. Journal of Biomedical Informatics. 2018; 77: 34–49.
11. Dudchenko A, Ganzinger M, Kopanitsa G. Diagnoses Detection in Short Snippets of Narrative Medical Texts. Procedia Computer Science. 2019; 156: 150–157.
12. Shanavas N, et al. Ontology-based enriched concept graphs for medical document classification. Inf. Sci. (Ny). 2020; 525: 172–181.
13. Oleynik Michel , et al. Evaluating shallow and deep learning strategies for the 2018 n2c2 shared task on clinical text classification. Journal of the American Medical Informatics Association. Oxford Academic. 2013; 26(11): 1247–1254.
14. Weng W-H, et al. Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach. BMC Med. Inform. Decis. Mak. 2017; 17(1): 155.
15. Tafti AP, et al. Big NN: An open-source big data toolkit focused on biomedical sentence classification. Proceedings–2017 IEEE International Conference on Big Data. Institute of Electrical and Electronics Engineers Inc. 2017; 2018: 3888–3896.
16. Ye Y, et al. Influenza detection from emergency department reports using natural language processing and Bayesian network classifiers. J. Am. Med. Informatics Assoc. BMJ Publishing Group. 2014; 21(5): 815–823.
2. Fung KW, et al. Using SNOMED CT-encoded problems to improve ICD-10-CM coding—A randomized controlled experiment. J. Med. Inform. Elsevier Ireland Ltd. 2019; 126: 19–25.
3. Fiebeck J, et al. Implementing LOINC: Current status and ongoing work at the Hannover Medical School. Studies in Health Technology and Informatics. IOS Press. 2019; 258: 247–248.
4. Mascia C, et al. OpenEHR modeling for genomics in clinical practice. J. Med. Inform. Elsevier Ireland Ltd. 2018; 120: 147–156.
5. Santos MR, Bax MP, Kalra D. Building a logical EHR architecture based on ISO 13606 standard and semantic web technologies. Studies in Health Technology and Informatics. IOS Press. 2010; 160(1): 161–165.
6. Ulrich H, et al. Metadata repository for improved data sharing and reuse based on HL7 FHIR. Studies in Health Technology and Informatics. IOS Press. 2017; 228: 162–166.
7. Huff S.M, et al. Integrating detailed clinical models into application development tools. Stud. Health Technol. Inform. IOS Press. 2004; 107: 1058–1062.
8. Hong N, et al. Standardizing Heterogeneous Annotation Corpora Using HL7 FHIR for Facilitating their Reuse and Integration in Clinical NLP.AMIA. Annu. Symp. proceedings. 2018; 2018: 574–583.
9. Lenivtceva ID, Kopanitsa G. Evaluating Manual Mappings of Russian Proprietary Formats and Terminologies to FHIR. Methods Inf. Med. 2019; 58: 4–5.
10. Wang Y, et al. Clinical information extraction applications: A literature review. Journal of Biomedical Informatics. 2018; 77: 34–49.
11. Dudchenko A, Ganzinger M, Kopanitsa G. Diagnoses Detection in Short Snippets of Narrative Medical Texts. Procedia Computer Science. 2019; 156: 150–157.
12. Shanavas N, et al. Ontology-based enriched concept graphs for medical document classification. Inf. Sci. (Ny). 2020; 525: 172–181.
13. Oleynik Michel , et al. Evaluating shallow and deep learning strategies for the 2018 n2c2 shared task on clinical text classification. Journal of the American Medical Informatics Association. Oxford Academic. 2013; 26(11): 1247–1254.
14. Weng W-H, et al. Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach. BMC Med. Inform. Decis. Mak. 2017; 17(1): 155.
15. Tafti AP, et al. Big NN: An open-source big data toolkit focused on biomedical sentence classification. Proceedings–2017 IEEE International Conference on Big Data. Institute of Electrical and Electronics Engineers Inc. 2017; 2018: 3888–3896.
16. Ye Y, et al. Influenza detection from emergency department reports using natural language processing and Bayesian network classifiers. J. Am. Med. Informatics Assoc. BMJ Publishing Group. 2014; 21(5): 815–823.
For citation
Lenivtceva ID, Kopanitsa GD. Automatic allergy classification based on Russian medical free texts. Medical doctor and information technology. 2021; 1: 18–24. (In Russ.). doi : 10.25881/ITP.2021.44.51.002.
Documents
Keywords