A smart medication recommendation model for the electronic prescription

Syed-Abdul, Shabbir; Nguyen, Alex; Huang, Frank; Jian, Wen-Shan; Iqbal, Usman; Yang, Vivian; Hsu, Min-Huei; Li, Yu-Chuan

doi:10.1016/j.cmpb.2014.06.019

Cited by 15 publications

(8 citation statements)

References 20 publications

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“…Early approaches often adopted instance-based methods, which only focused on current health conditions and failed to make full use of historical information. Syed-Abdul et al [ 33 ] proposed a smart medication recommendation model for the electronic prescription. In order to reduce the probability of illegal prescription, this smart model adopted the association rule mining technology to find the relationship between two labels for reducing the probability of illegal prescription.…”

Section: Related Workmentioning

confidence: 99%

MR-KPA: medication recommendation by combining knowledge-enhanced pre-training with a deep adversarial network

et al. 2022

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Background Medication recommendation based on electronic medical record (EMR) is a research hot spot in smart healthcare. For developing computational medication recommendation methods based on EMR, an important challenge is the lack of a large number of longitudinal EMR data with time correlation. Faced with this challenge, this paper proposes a new EMR-based medication recommendation model called MR-KPA, which combines knowledge-enhanced pre-training with the deep adversarial network to improve medication recommendation from both feature representation and the fine-tuning process. Firstly, a knowledge-enhanced pre-training visit model is proposed to realize domain knowledge-based external feature fusion and pre-training-based internal feature mining for improving the feature representation. Secondly, a medication recommendation model based on the deep adversarial network is developed to optimize the fine-tuning process of pre-training visit model and alleviate over-fitting of model caused by the task gap between pre-training and recommendation. Result The experimental results on EMRs from medical and health institutions in Hainan Province, China show that the proposed MR-KPA model can effectively improve the accuracy of medication recommendation on small-scale longitudinal EMR data compared with existing representative methods. Conclusion The advantages of the proposed MR-KPA are mainly attributed to knowledge enhancement based on ontology embedding, the pre-training visit model and adversarial training. Each of these three optimizations is very effective for improving the capability of medication recommendation on small-scale longitudinal EMR data, and the pre-training visit model has the most significant improvement effect. These three optimizations are also complementary, and their integration makes the proposed MR-KPA model achieve the best recommendation effect.

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Section: Related Workmentioning

confidence: 99%

MR-KPA: medication recommendation by combining knowledge-enhanced pre-training with a deep adversarial network

et al. 2022

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“…AI softwares process electronic patient records and identify those with systemic sclerosis with the risk of scleroderma renal crisis 55 . In computerized drug prescription systems, AI improves system efficiency and reduces the risk of wrong drug choice by a physician 56 .…”

Section: Overview Of Ai Application In Clinical and Dental Medicinementioning

confidence: 99%

Artificial intelligence in clinical medicine and dentistry

et al. 2017

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“…Another example is applying in the medical prescription. Although the health professionals prescribed for treating diseases are diversity, mining of those data from a nationwide prescription database will be a great benefit to develop a smart medication recommendation model to reduce costs and improve patient safety [27].…”

Section: Enhanced Health Care With Better Miningmentioning

confidence: 99%