MERITS: Medication Recommendation for Chronic Disease with Irregular Time-Series

Zhang, Shuai; Li, Jianxin; Zhou, Haoyi; Zhu, Qishan; Zhang, Shanghang; Wang, Danding

doi:10.1109/icdm51629.2021.00192

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…MICRON [8] is a model designed for predicting the change in prescriptions, it models the change of prescribed medications with residual vectors. In addition to a patient's EHR, MERITS [28] used the neural ordinary differential equation to model the irregular time series of the patient's vital signs. The model proposed by Yao et al [29] used RNN to model the path from the root node to medical concepts on medical ontologies.…”

Section: Longitudinal Medication Recommendationmentioning

confidence: 99%

OntoMedRec: Logically-pretrained model-agnostic ontology encoders for medication recommendation

Tan,

Wang,

Zhou

et al. 2024

World Wide Web

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Recommending medications with electronic health records (EHRs) is a challenging task for data-driven clinical decision support systems. Most existing models learnt representations for medical concepts based on EHRs and make recommendations with the learnt representations. However, most medications appear in EHR datasets for limited times (the frequency distribution of medications follows power law distribution), resulting in insufficient learning of their representations of the medications. Medical ontologies are the hierarchical classification systems for medical terms where similar terms will be in the same class on a certain level. In this paper, we propose OntoMedRec, the logically-pretrained and model-agnostic medical Ontology Encoders for Medication Recommendation that addresses data sparsity problem with medical ontologies. We conduct comprehensive experiments on real-world EHR datasets to evaluate the effectiveness of OntoMedRec by integrating it into various existing downstream medication recommendation models. The result shows the integration of OntoMedRec improves the performance of various models in both the entire EHR datasets and the admissions with few-shot medications. We provide the GitHub repository for the source code. (https://github.com/WaicongTam/OntoMedRec)

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Section: Longitudinal Medication Recommendationmentioning

confidence: 99%

OntoMedRec: Logically-pretrained model-agnostic ontology encoders for medication recommendation

Tan,

Wang,

Zhou

et al. 2024

World Wide Web

View full text Add to dashboard Cite

show abstract

“…In order to improve the accuracy of recommendation, related studies mainly adopted longitudinal sequential recommendation methods which integrated patient's current health conditions and historical visit information to effectively leverage the temporal dependencies among clinical events for medication recommendation [13,17]. Recent studies focused on developing novel and complex neural networks to capture deep-level data features, including complete structure information [11], drugdrug interactions [12], multiple-level importance [18], relationships between historical and current diagnoses [19], irregular time-series dependencies [20], for improving recommendation capabilities.…”

Section: Introductionmentioning

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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SHAPE: A Sample-Adaptive Hierarchical Prediction Network for Medication Recommendation

Liu,

Wang,

et al. 2023

IEEE J. Biomed. Health Inform.

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Effectively medication recommendation with complex multimorbidity conditions is a critical task in healthcare. Most existing works predicted medications based on longitudinal records, which assumed the information transmitted patterns of learning longitudinal sequence data are stable and intra-visit medical events are serialized. However, the following conditions may have been ignored: 1) A more compact encoder for intra-relationship in the intra-visit medical event is urgent; 2) Strategies for learning accurate representations of the variable longitudinal sequences of patients are different. In this paper, we proposed a novel Sample-adaptive Hierarchical med-icAtion Prediction nEtwork, termed SHAPE, to tackle the above challenges in the medication recommendation task. Specifically, we design a compact intra-visit set encoder to encode the relationship in the medical event for obtaining visit-level representation and then develop an inter-visit longitudinal encoder to learn the patient-level longitudinal representation efficiently. To endow the model with the capability of modeling the variable visit length, we introduce a soft curriculum learning method to assign the difficulty of each sample automatically by the visit length. Extensive experiments on a benchmark dataset verify the superiority of our model compared with several state-of-the-art baselines.

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MERITS: Medication Recommendation for Chronic Disease with Irregular Time-Series

Cited by 3 publications

References 7 publications

OntoMedRec: Logically-pretrained model-agnostic ontology encoders for medication recommendation

OntoMedRec: Logically-pretrained model-agnostic ontology encoders for medication recommendation

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

SHAPE: A Sample-Adaptive Hierarchical Prediction Network for Medication Recommendation

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