Machine learning-based prediction model for emergency department visits using prescription information in community-dwelling non-cancer older adults
Abstract Older adults are more likely to require emergency department (ED) visits than others, which might be attributed to their medication use. Being able to predict the likelihood of an ED visit using prescription information and readily available data would be useful for primary care. This study...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-46094-z |
| _version_ | 1797636938814980096 |
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| author | Soyoung Park Changwoo Lee Seung-Bo Lee Ju-yeun Lee |
| author_facet | Soyoung Park Changwoo Lee Seung-Bo Lee Ju-yeun Lee |
| author_sort | Soyoung Park |
| collection | DOAJ |
| description | Abstract Older adults are more likely to require emergency department (ED) visits than others, which might be attributed to their medication use. Being able to predict the likelihood of an ED visit using prescription information and readily available data would be useful for primary care. This study aimed to predict the likelihood of ED visits using extensive medication variables generated according to explicit clinical criteria for elderly people and high-risk medication categories by applying machine learning (ML) methods. Patients aged ≥ 65 years were included, and ED visits were predicted with 146 variables, including demographic and comprehensive medication-related factors, using nationwide claims data. Among the eight ML models, the final model was developed using LightGBM, which showed the best performance. The final model incorporated 93 predictors, including six sociodemographic, 28 comorbidity, and 59 medication-related variables. The final model had an area under the receiver operating characteristic curve of 0.689 in the validation cohort. Approximately half of the top 20 strong predictors were medication-related variables. Here, an ED visit risk prediction model for older people was developed and validated using administrative data that can be easily applied in clinical settings to screen patients who are likely to visit an ED. |
| first_indexed | 2024-03-11T12:42:22Z |
| format | Article |
| id | doaj.art-0604d2c32cf24a009fd6901c14e80eec |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-11T12:42:22Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-0604d2c32cf24a009fd6901c14e80eec2023-11-05T12:12:17ZengNature PortfolioScientific Reports2045-23222023-11-0113111010.1038/s41598-023-46094-zMachine learning-based prediction model for emergency department visits using prescription information in community-dwelling non-cancer older adultsSoyoung Park0Changwoo Lee1Seung-Bo Lee2Ju-yeun Lee3College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National UniversityDepartment of Transdisciplinary Medicine, Seoul National University HospitalDepartment of Medical Informatics, Keimyung University School of MedicineCollege of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National UniversityAbstract Older adults are more likely to require emergency department (ED) visits than others, which might be attributed to their medication use. Being able to predict the likelihood of an ED visit using prescription information and readily available data would be useful for primary care. This study aimed to predict the likelihood of ED visits using extensive medication variables generated according to explicit clinical criteria for elderly people and high-risk medication categories by applying machine learning (ML) methods. Patients aged ≥ 65 years were included, and ED visits were predicted with 146 variables, including demographic and comprehensive medication-related factors, using nationwide claims data. Among the eight ML models, the final model was developed using LightGBM, which showed the best performance. The final model incorporated 93 predictors, including six sociodemographic, 28 comorbidity, and 59 medication-related variables. The final model had an area under the receiver operating characteristic curve of 0.689 in the validation cohort. Approximately half of the top 20 strong predictors were medication-related variables. Here, an ED visit risk prediction model for older people was developed and validated using administrative data that can be easily applied in clinical settings to screen patients who are likely to visit an ED.https://doi.org/10.1038/s41598-023-46094-z |
| spellingShingle | Soyoung Park Changwoo Lee Seung-Bo Lee Ju-yeun Lee Machine learning-based prediction model for emergency department visits using prescription information in community-dwelling non-cancer older adults Scientific Reports |
| title | Machine learning-based prediction model for emergency department visits using prescription information in community-dwelling non-cancer older adults |
| title_full | Machine learning-based prediction model for emergency department visits using prescription information in community-dwelling non-cancer older adults |
| title_fullStr | Machine learning-based prediction model for emergency department visits using prescription information in community-dwelling non-cancer older adults |
| title_full_unstemmed | Machine learning-based prediction model for emergency department visits using prescription information in community-dwelling non-cancer older adults |
| title_short | Machine learning-based prediction model for emergency department visits using prescription information in community-dwelling non-cancer older adults |
| title_sort | machine learning based prediction model for emergency department visits using prescription information in community dwelling non cancer older adults |
| url | https://doi.org/10.1038/s41598-023-46094-z |
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