Personalized Prescription Recommendation Using Attention Over Medical Order Information
In the context of patients with complex conditions, the capacity to assist physicians in making appropriate prescribing decisions is of paramount importance. The field of prescription recommendation has attracted a growing body of research interest, given its significant clinical value. However, exi...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10679140/ |
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| _version_ | 1850060728866701312 |
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| author | Feng Gao Na Zhao Yao Chen Jianjun Guo Yongqing Wang Hongyu Yuan Shan Lu |
| author_facet | Feng Gao Na Zhao Yao Chen Jianjun Guo Yongqing Wang Hongyu Yuan Shan Lu |
| author_sort | Feng Gao |
| collection | DOAJ |
| description | In the context of patients with complex conditions, the capacity to assist physicians in making appropriate prescribing decisions is of paramount importance. The field of prescription recommendation has attracted a growing body of research interest, given its significant clinical value. However, existing research has not taken into account the patients’ intention to visit the doctor and the similarities between patients’ visit intentions. In this study, we employ medical order data in both prescription intention modeling and patient similarity analysis, subsequently integrating these to generate more personalized and accurate prescription recommendations. The cross-attention mechanism is first employed to focus the representation of the patient’s health status on the patient’s chief complaints, with the objective of predicting treatment intention and, subsequently, the prescriptions. Subsequently, when we learn from the successful treatment experiences of similar patients, we focus more on the similarities of their chief complaints. The experimental results on the MIMIC-III dataset demonstrate that our method achieves optimal performance in terms of Jaccard score, PRAUC, F1 score and average precision, with relative improvements of 7%, 5%, 4%, and 8% respectively, in comparison to state-of-the-art approaches. |
| format | Article |
| id | doaj-art-1a019e5925c74412b2f2c5aff3e09e8b |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1a019e5925c74412b2f2c5aff3e09e8b2025-08-20T02:50:29ZengIEEEIEEE Access2169-35362024-01-011217224417225510.1109/ACCESS.2024.345908010679140Personalized Prescription Recommendation Using Attention Over Medical Order InformationFeng Gao0https://orcid.org/0000-0002-2396-1360Na Zhao1https://orcid.org/0009-0009-7306-5802Yao Chen2https://orcid.org/0009-0008-4561-095XJianjun Guo3Yongqing Wang4Hongyu Yuan5Shan Lu6Department of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, ChinaDepartment of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, ChinaDepartment of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, ChinaThe First Affiliated Hospital, Nanjing Medical University (Jiangsu Province Hospital), Nanjing, ChinaThe First Affiliated Hospital, Nanjing Medical University (Jiangsu Province Hospital), Nanjing, ChinaThe First Affiliated Hospital, Nanjing Medical University (Jiangsu Province Hospital), Nanjing, ChinaThe First Affiliated Hospital, Nanjing Medical University (Jiangsu Province Hospital), Nanjing, ChinaIn the context of patients with complex conditions, the capacity to assist physicians in making appropriate prescribing decisions is of paramount importance. The field of prescription recommendation has attracted a growing body of research interest, given its significant clinical value. However, existing research has not taken into account the patients’ intention to visit the doctor and the similarities between patients’ visit intentions. In this study, we employ medical order data in both prescription intention modeling and patient similarity analysis, subsequently integrating these to generate more personalized and accurate prescription recommendations. The cross-attention mechanism is first employed to focus the representation of the patient’s health status on the patient’s chief complaints, with the objective of predicting treatment intention and, subsequently, the prescriptions. Subsequently, when we learn from the successful treatment experiences of similar patients, we focus more on the similarities of their chief complaints. The experimental results on the MIMIC-III dataset demonstrate that our method achieves optimal performance in terms of Jaccard score, PRAUC, F1 score and average precision, with relative improvements of 7%, 5%, 4%, and 8% respectively, in comparison to state-of-the-art approaches.https://ieeexplore.ieee.org/document/10679140/Prescription recommendationelectronic health recordpatient similarity analysisattention mechanism |
| spellingShingle | Feng Gao Na Zhao Yao Chen Jianjun Guo Yongqing Wang Hongyu Yuan Shan Lu Personalized Prescription Recommendation Using Attention Over Medical Order Information IEEE Access Prescription recommendation electronic health record patient similarity analysis attention mechanism |
| title | Personalized Prescription Recommendation Using Attention Over Medical Order Information |
| title_full | Personalized Prescription Recommendation Using Attention Over Medical Order Information |
| title_fullStr | Personalized Prescription Recommendation Using Attention Over Medical Order Information |
| title_full_unstemmed | Personalized Prescription Recommendation Using Attention Over Medical Order Information |
| title_short | Personalized Prescription Recommendation Using Attention Over Medical Order Information |
| title_sort | personalized prescription recommendation using attention over medical order information |
| topic | Prescription recommendation electronic health record patient similarity analysis attention mechanism |
| url | https://ieeexplore.ieee.org/document/10679140/ |
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