A Recommendation System for Anti-Diabetic Drugs Selection Based on Fuzzy Reasoning and Ontology Techniques

Chen, Shyi‐Ming; Huang, Yun-Hou; Chen, Rung-Ching

doi:10.1142/s0218001413590015

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…As a chronic and progressive disease, there are many different medicines available for diabetes, and many current personalized medicine recommendation models are targeted at diabetes. Chen et al [4] developed a decision support system that uses an approach based on multi-criteria decision-making and domain ontology. Subsequently, Chen et al [5] and Mahmoud and Elbeh [6] proposed diabetes medicine recommendations system based on fuzzy reasoning and ontology technology.…”

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.

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“…The difference between this study and previous research is that this study uses more complex parameters to recommend the type of drug and its name. Also, being able to calculate the dosage and frequency based on parameters so that the dose and frequency are more precise and consider the price and efficacy of the drug [12] Shyi-Ming Chen et al [13] Rung Ching Chen et al [14] M. Eghbali et al […”

Section: Comparison With Existing Systemmentioning

confidence: 99%

“…In the study showed Rung-Ching Chen et al [12], the drug recommendations used the SWRL technique with 6 (six) types of antidiabetic drugs Metformin, DPP4, Sulfonylurea, Glinide, Thiazolidinedione, Alpha-Glucosidase (AGI) with 6 (six) parameters of HbA1c, Hypoglycemia, Renal, Heart, BMI, and liver. This research was developed with the Fuzzy method that can display the results of drug recommendations based on the most appropriate level of choice [13]. Drug recommendations are also carried out using Fuzzy-TOPSIS with 7 (seven) types of drugs and 8 (eight) parameters [14].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Based Application Model and Profile Matching for Recommendation Suitability of Type 2 Diabetic

Wantoro¹,

Syarif²,

Muludi³

et al. 2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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Diabetes Mellitus (DM) is a metabolic disease characterized by hyperglycemia due to insulin secretion abnormalities and a global health threat. DM has several types, namely type 1, 2, gestational, and other types. Type 2 diabetes patients have the largest number in the world. DM therapy can be done in 2 (two) ways: improving lifestyle and administering drugs. The problems and risks in recommending drugs are essential in the patient's healing process because they are likely to take medicine for life. Approximately 260,000 patients with type 2 diabetes experienced medication errors in 2017. The doctor's mistake in recommending drugs causes a long healing process and costs more. Recommending drugs requires pharmacological knowledge, and not all hospitals have pharmacologists. Several researchers have researched recommendations for antidiabetic drugs, but no studies have yet been found that discuss recommendations for combination antidiabetic drugs for type two to determine dosage and frequency. The number of medications used is 6 to 7, with many parameters 5 to 8. The latest endocrinology guidelines for 2020 state that in recommending antidiabetic drugs, not only 6 to 7 participants, but still need to maintain other aspects. Therefore, this study aims to build an expert system model with a new approach in recommending antidiabetic drugs with more complete parameters and recommend dosage and frequency. The model developed uses the Fuzzy Profile Matching method. Fuzzy is used to calculate the suitability between the patient's condition and the type of antidiabetic drug. Profile Matching is used to calculate the core factor and secondary factor to obtain each drug's total value.The dose was calculated using the FIS Tsukamoto for inputting low dosage, and high dosage calculated the weighted average value. Determination of frequency using the IF-Then function. Model evaluation is done by comparing recommendation data from doctors.The results of the evaluation of the model obtained an accuracy of 90%. This system will reduce medical personnel errors in recommending antidiabetic drugs that can positively impact patients' time, the healing process, and costs. This study provides knowledge that antidiabetes drugs' determination requires many parameters, while other studies used only 4 to 8. This study also provides an overview of the dosages of drugs that drug companies can produce. Usually, the company only makes low and high dosage. This study shows that creating multiple drug dosage is more efficient for patients.

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“…Ontologies also are used in many applications, e.g., entertainment [11][12], ecommerce [13][14], nutrition [15], medicine [16][17][18], services [19][20], and etc.…”

Section: Ontologymentioning

confidence: 99%

“…Chen et al [17] presented a system to recommend anti-diabetics drugs which is based on fuzzy reasoning. Fuzzy rules have been used to represent knowledge in order to infer the usability of the classes of anti-diabetic drugs based on fuzzy reasoning techniques.…”

Section: Ontology-based Recommender Systemsmentioning

confidence: 99%

An Ontology-Based Recommender System Architecture for Semantic Searches in Vehicles Sales Portals

Paiva

Costa

Silva

2014

Lecture Notes in Computer Science

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Abstract.Internet has become an increasingly constant presence everywhere that people go. Particularly this reality is visible in social networks and selling portals scenarios. Whatever scenario, there is plenty of space to improve accuracy since big data is a problem when scale increases. Semantic search is an alternative to improve search accuracy by understanding the contextual meaning of terms as they appear in the searchable data space. Among the several approaches to Semantic Search methodologies, a variation of Ontologybased search (or Logic Approach) is the one adopted. In this methodology, the engine not only understands hierarchical relationships of entities, however also more complex inter-entities relationships defined inside ontologies. This paper proposes a hybrid approach for the problem using Ontology-based Recommender Systems and semantic profiles. A portal prototype is designed and implemented for the domain of online dealership's vehicle buyer's market. Precision and Recall measures are the two major indices of information retrieval. They have been used to evaluate the prototype results. After calculating these two metrics over some searches, we have seen that Precision is 86.66% and Recall is 68.42%. These final results have demonstrated an improvement in the searches, particularly with regard the precision of the results provided to the users.

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A Recommendation System for Anti-Diabetic Drugs Selection Based on Fuzzy Reasoning and Ontology Techniques

Cited by 9 publications

References 12 publications

A Survey of Personalized Medicine Recommendation

A Survey of Personalized Medicine Recommendation

Fuzzy-Based Application Model and Profile Matching for Recommendation Suitability of Type 2 Diabetic

An Ontology-Based Recommender System Architecture for Semantic Searches in Vehicles Sales Portals

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