Drug Recommendation System for Geriatric Patients Based on Bayesian Networks and Evolutionary Computation

Montalvo, Lourdes; Villanueva, Edwin

doi:10.1007/978-3-030-39512-4_77

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Shang et al [19] introduced the graph structure into the personalized medicine recommendation problem for the first time by integrating the knowledge graph of medicine-medicine interactions as a memory module of the graph convolutional network and modeling longitudinal patient records as queries. Wang [20] used the power of Generative Adversarial Networks (GANs) to learn expressive representations from a specific patient's medical records and provide accurate medicine recommendations to the patient. Montalvo and Villanueva [21] proposed a medicine recommendation system for geriatric patients that takes into account a patient's medical history and medicine interactions.…”

Section: Medicine Recommendation Based On Multi-diseasementioning

confidence: 99%

A Survey of Personalized Medicine Recommendation

Zhu,

Cui,

et al. 2024

Int. J. Crowd Sci.

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Mining potential and valuable medical knowledge from massive medical data to support clinical decision-making has become an important research field. Personalized medicine recommendation is an important research direction in this field, aiming to recommend the most suitable medicines for each patient according to the health status of the patient. Personalized medicine recommendation can assist clinicians to make clinical decisions and avoid the occurrence of medical abnormalities, so it has been widely concerned by many researchers. Based on this, this paper makes a comprehensive review of personalized medicine recommendation. Specifically, we first make clear the definition of personalized medicine recommendation problem; then, starting from the key theories and technologies, the personalized medicine recommendation algorithms proposed in recent years are systematically classified (medicine recommendation based on multi-disease, medicine recommendation with combination pattern, medicine recommendation with additional knowledge, and medicine recommendation based on feedback) and in-depth analyzed; and this paper also introduces how to evaluate personalized medicine recommendation algorithms and some common evaluation indicators; finally, the challenges of personalized medicine recommendation problem are put forward, and the future research direction and development trends are prospected.

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Section: Medicine Recommendation Based On Multi-diseasementioning

confidence: 99%

A Survey of Personalized Medicine Recommendation

Zhu,

Cui,

et al. 2024

Int. J. Crowd Sci.

View full text Add to dashboard Cite

show abstract

“…Drug-drug interactions, co-occurring diseases, and existing symptoms were all taken into account by the drug recommendation system for the elderly, which was proposed by Montalvo and Villanueva (2020). In the proposed system, a model of the geriatric patient was developed using a Bayesian network that permits inferences to be drawn with limited data about patient.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Recommendation Model for Drug Selection

KHARMA,

SHAMBOUR,

HUSSEIN

2023

SIC

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Medical errors associated with medication pose significant threats to patients' safety, primarily due to the abundance of drug information available on various online healthcare platforms, leading to challenges in identifying relevant drugs. To address this issue, drug recommendation systems have been developed to assist in selecting appropriate medications for specific medical conditions. Collaborative filtering approaches have been widely used to generate personalized recommendations for various applications. They are easy to implement, debug, and provide justifiable reasoning for recommended items, which is not readily accessible in several other recommendation approaches. Regardless of their success, they still need further enhancements to address challenges related to insufficient rating data, such as data sparsity and new item problems. This paper proposes a drug recommendation model that effectively employs drug taxonomy and multi-criteria collaborative filtering to tackle these challenges. Drug taxonomy enhances recommendation quality by offering a more organized and granular representation of drugs, while multi-criteria rating captures the patients' preferences more accurately, enabling accurate recommendations that better match the patient's specific preferences. Experiments conducted on a real-world drug multi-criteria rating dataset demonstrate that the proposed model outperforms baseline recommendation approaches in addressing these challenges and improving prediction accuracy and coverage, making it a valuable tool to assist patients in selecting relevant drugs for their specific medical conditions.

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Drug Recommendation System for Geriatric Patients Based on Bayesian Networks and Evolutionary Computation

Cited by 2 publications

References 9 publications

A Survey of Personalized Medicine Recommendation

A Survey of Personalized Medicine Recommendation

A Hybrid Recommendation Model for Drug Selection

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