Information Extraction from Electronic Medical Records using Natural Language Processing Techniques
Patients share key information about their health with medical practitioners during clinic consultations. These key information may include their past medications and allergies, current situations/issues, and expectations. The healthcare professionals store this information in an Electronic Medical...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Joint Coordination Centre of the World Bank assisted National Agricultural Research Programme (NARP)
2020-07-01
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| Series: | Journal of Applied Sciences and Environmental Management |
| Subjects: | |
| Online Access: | https://www.ajol.info/index.php/jasem/article/view/197680 |
| _version_ | 1797228136066187264 |
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| author | E. Ogbuju G.N. Obunadike |
| author_facet | E. Ogbuju G.N. Obunadike |
| author_sort | E. Ogbuju |
| collection | DOAJ |
| description |
Patients share key information about their health with medical practitioners during clinic consultations. These key information may include their past medications and allergies, current situations/issues, and expectations. The healthcare professionals store this information in an Electronic Medical Record (EMR). EMRs have empowered research in healthcare; information hidden in them if harnessed properly through Natural Language Processing (NLP) can be used for disease registries, drug safety, epidemic surveillance, disease prediction, and treatment. This work illustrates the application of NLP techniques to design and implement a Key Information Retrieval System (KIRS framework) using the Latent Dirichlet Allocation algorithm. The cross-industry standard process for data mining methodology was applied in an experiment with an EMR dataset from PubMed to
demonstrate the framework. The new system extracted the common problems (ailments) and prescriptions across the five (5) countries presented in the dataset. The system promises to assist health organizations in making informed decisions with the flood of key information data available in their domain.
Keywords: Electronic Medical Record, BioNLP, Latent Dirichlet Allocation
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| first_indexed | 2024-04-24T14:51:54Z |
| format | Article |
| id | doaj.art-e704e70eb4514dbd8e1f314fc71b095c |
| institution | Directory Open Access Journal |
| issn | 2659-1502 2659-1499 |
| language | English |
| last_indexed | 2024-04-24T14:51:54Z |
| publishDate | 2020-07-01 |
| publisher | Joint Coordination Centre of the World Bank assisted National Agricultural Research Programme (NARP) |
| record_format | Article |
| series | Journal of Applied Sciences and Environmental Management |
| spelling | doaj.art-e704e70eb4514dbd8e1f314fc71b095c2024-04-02T19:49:14ZengJoint Coordination Centre of the World Bank assisted National Agricultural Research Programme (NARP)Journal of Applied Sciences and Environmental Management2659-15022659-14992020-07-0124610.4314/jasem.v24i6.13Information Extraction from Electronic Medical Records using Natural Language Processing TechniquesE. OgbujuG.N. Obunadike Patients share key information about their health with medical practitioners during clinic consultations. These key information may include their past medications and allergies, current situations/issues, and expectations. The healthcare professionals store this information in an Electronic Medical Record (EMR). EMRs have empowered research in healthcare; information hidden in them if harnessed properly through Natural Language Processing (NLP) can be used for disease registries, drug safety, epidemic surveillance, disease prediction, and treatment. This work illustrates the application of NLP techniques to design and implement a Key Information Retrieval System (KIRS framework) using the Latent Dirichlet Allocation algorithm. The cross-industry standard process for data mining methodology was applied in an experiment with an EMR dataset from PubMed to demonstrate the framework. The new system extracted the common problems (ailments) and prescriptions across the five (5) countries presented in the dataset. The system promises to assist health organizations in making informed decisions with the flood of key information data available in their domain. Keywords: Electronic Medical Record, BioNLP, Latent Dirichlet Allocation https://www.ajol.info/index.php/jasem/article/view/197680Electronic Medical Record, BioNLP, Latent Dirichlet Allocation |
| spellingShingle | E. Ogbuju G.N. Obunadike Information Extraction from Electronic Medical Records using Natural Language Processing Techniques Journal of Applied Sciences and Environmental Management Electronic Medical Record, BioNLP, Latent Dirichlet Allocation |
| title | Information Extraction from Electronic Medical Records using Natural Language Processing Techniques |
| title_full | Information Extraction from Electronic Medical Records using Natural Language Processing Techniques |
| title_fullStr | Information Extraction from Electronic Medical Records using Natural Language Processing Techniques |
| title_full_unstemmed | Information Extraction from Electronic Medical Records using Natural Language Processing Techniques |
| title_short | Information Extraction from Electronic Medical Records using Natural Language Processing Techniques |
| title_sort | information extraction from electronic medical records using natural language processing techniques |
| topic | Electronic Medical Record, BioNLP, Latent Dirichlet Allocation |
| url | https://www.ajol.info/index.php/jasem/article/view/197680 |
| work_keys_str_mv | AT eogbuju informationextractionfromelectronicmedicalrecordsusingnaturallanguageprocessingtechniques AT gnobunadike informationextractionfromelectronicmedicalrecordsusingnaturallanguageprocessingtechniques |