Identifying Association Rules among Drugs in Prescription of a Single Drugstore Using Apriori Method

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ISSN Print: 2160-5912
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"Identifying Association Rules among Drugs in Prescription of a Single Drugstore Using Apriori Method"
written by Ahmad Yoosofan, Fatemeh Ghovanlooy Ghajar, Sima Ayat, Somayeh Hamidi, Farshad Mahini,
published by Intelligent Information Management, Vol.7 No.5, 2015

has been cited by the following article(s):

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[1]	Discovering hidden patterns among medicines prescribed to patients using Association Rule Mining Technique
	International Journal of Healthcare …, 2022

[2]	Using association rule mining for finding drug-drug interaction patterns of warfarin for improving computerized physician order entry.
	2022

[3]	Efficacy-specific Herbal Group Detection from Traditional Chinese Medicine Prescriptions via Hierarchical Attentive Neural Network Model
	2020

[4]	Застосування ABC-Аналізу для Відсікання Неінформативних Наборів при Паралельному Пошуку Асоціацій
	Інформаційні технології в …, 2019

[5]	Penerapan Association Rules Menggunakan Algoritma Apriori Pada Sistem Rekomendasi Pemilihan Resep Obat Berdasarkan Data Rekam Medis
	2018

[6]	АЛГОРИТМИЧЕСКАЯ ПОДДЕРЖКА ПАРАЛЛЕЛЬНЫХ МЕТОДОВ ПОИСКА АССОЦИАТИВНЫХ ПРАВИЛ
	2018

[7]	Evaluation of rational nonsteroidal anti-inflammatory drugs and gastro-protective agents use; association rule data mining using outpatient prescription …
	2017

[8]	Интеллектуальный анализ данных в фармацевтическом бизнесе
	2016

[9]	USING DATA MINING TO DEVELOP MODEL FOR CLASSIFYING DIABETIC PATIENT CONTROL LEVEL BASED ON HISTORICAL MEDICAL RECORDS
	Journal of Theoretical & Applied Information Technology, 2016

[10]	USING DATA MINING TO DEVELOP MODEL FOR CLASSIFYING DIABETIC PATIENT CONTROL LEVEL BASED ON HISTORICAL MEDICAL RECORDS.
	2016

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[1]	Survey, Analysis and Association Rules derivation using Apriori Method for buying preference amongst kids of age-group 5 to 9 in India 2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS), 2023 DOI:10.1109/PCEMS58491.2023.10136055