RAFTS: A T-38 Replacement for the 21st Century

Chapman, J. A.

doi:10.21236/ada156103

Cited by 24 publications

(46 citation statements)

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“…In the first stage, prescriptions were processed depending on Cross Industry Standard Practice for Data Mining (CRISP-DM) methodology. The CRISP-DM methodology helps to understand data mining process to make large data mining projects, less costly, more reliable, and faster [28]. Then, the drugs were classified into groups according to ATC drug classification system.…”

Section: Methodsmentioning

confidence: 99%

A New Drug-Shelf Arrangement for Reducing Medication Errors using Data Mining: A Case Study

Ceylan¹,

Fırat²

2017

SDÜ Fen Bil Enst Der

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Medication errors are common, fatal, costly but preventable. Location of drugs on the shelves and wrong drug names in prescriptions can cause errors during dispensing process. Therefore, a good drug-shelf arrangement system in pharmacies is crucial for preventing medication errors, increasing patient's safety, evaluating pharmacy performance, and improving patient outcomes. The main purpose of this study to suggest a new drug-shelf arrangement for the pharmacy to prevent wrong drug selection from shelves by the pharmacist. The study proposes an integrated structure with three-stage data mining method using patient prescription records in database. In the first stage, drugs on prescriptions were clustered depending on the Anatomical Therapeutic Chemical (ATC) classification system to determine associations of drug utilizations. In the second stage association rule mining (ARM), wellknown data mining technique, was applied to obtain frequent association rules between drugs which tend to be purchased together. In the third stage, the generated rules from ARM were used in multidimensional scaling (MDS) analysis to create a map displaying the relative location of drug groups on pharmacy shelves. The results of study showed that data mining is a valuable and very efficient tool which provides a basis for potential future investigation to enhance patient safety. Veri Madenciliği Kullanarak Tıbbi Hataların Azaltılması için Yeni Bir İlaç-Raf Düzeni:Bir Vaka Analizi Anahtar kelimeler Birliktelik kuralları, Veri madenciliği, İlaç-raf düzeni, Tıbbi hatalar, Çok boyutlu ölçekleme Özet: Tıbbi hatalar yaygın, ölümcül ve maliyetli ama önlenebilirlerdir. İlaçların raflardaki konumu ve reçetelerdeki yanlış ilaç isimleri, ilaçların dağıtım sürecindeki hataları arttırabilir. Bu nedenle, eczanelerde iyi bir ilaç-raf düzenleme sistemi tıbbi hataların önlenmesi, hasta güvenliğinin arttırılması, eczane performansının değerlendirilmesi ve hasta geri-dönüşlerinin iyileştirilmesi açısından hayati önem taşımaktadır. Bu çalışmanın temel amacı, eczacı tarafından raflardan yanlış ilaç seçimini önlemek üzere eczane için yeni bir ilaç-raf düzeni önermektir. Çalışma, 3-aşamalı veri madenciliği metodu ile veri tabanındaki hasta reçete kayıtlarını kullanarak bir entegre yapı önermektedir. İlk aşamada, reçetelerdeki ilaçlar, ilaç kullanımları arasındaki ilişkileri belirlemek üzere Anatomik Terapötik Kimyasal (ATK) sınıflandırma sistemine göre sınıflandırıldı. İkinci aşamada, iyi bilinen bir veri madenciliği tekniği olan Birliktelik Kural Madenciliği (BKM), birlikte satın alınma eğilimindeki ilaçlar arasındaki sık rastlanan birliktelik kurallarını elde etmek için uygulandı. Üçüncü aşamada, BKM ile üretilen kurallar, eczane raflarında ilaçların göreceli olarak yerlerini gösteren haritayı oluşturmak için Çok Boyutlu Ölçekleme (ÇBÖ) analizinde kullanıldı. Çalışmanın sonuçları, veri madenciliğinin hasta güvenliğinin arttırılması üzerine gelecekte yapılacak araştırmalar için temel oluşturan değerli ve çok verimli bir araç olduğunu göstermiştir.

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Section: Methodsmentioning

confidence: 99%

A New Drug-Shelf Arrangement for Reducing Medication Errors using Data Mining: A Case Study

Ceylan¹,

Fırat²

2017

SDÜ Fen Bil Enst Der

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“…Gregor et.al, [74] presented a framework for process mining in critical care. The CRoss Industry Standard Process for Data Mining (CRISP-DM) [75] was developed in 1996, with the goal of being industry, tool and application-neutral. Constant references to the methodology by analysts have established it as the de facto standard for data mining and Knowledge Discovery in Databases (KDD) [76].…”

Section: Clinical Data Mining Frameworkmentioning

confidence: 99%

Data Mining in Clinical Data Sets: A Review

GraciaJacob¹,

Ramani²

2012

IJAIS

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Data mining is one of the extensively researched areas in computer science and information technology owing to the wide influence exhibited by this computational technique on diverse fields that include finance, clinical research, multimedia, education and the like. Adequate survey and literature has been devoted to Clinical data mining, an active interdisciplinary area of research that is considered the consequent of applying artificial intelligence and data mining concepts to the field of medicine and health care. The aim of this research work is to provide a review on the foundation principles of mining clinical datasets, and present the findings and results of past researches on utilizing data mining techniques to mine health care data and patient records. The scope of this article is to present a brief report on preceding investigations made in the sphere of mining clinical data, the techniques applied and the conclusions recounted. Albeit extensive research has led to remarkable advancement in the field of clinical data mining and has paved the way for incredible enhancements in medical practice, the most recent research findings that can further unveil the potential of data mining in the realm of health care and medicine are clearly presented in this review.

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“…In object modelling, when we assign OIDs to both row objects and column objects, new complex data types (domains) may be constructed by using certain type constructors. For example, a set type is defined as a unordered set of OIDs {o 1 ,……….,o M }, which stand for the M objects that are typically of the same class; a tuple type has the form < A 1 Reference attributes that refer to other objects serve to represent relationships among the classes. The value of such an attribute is set to be the OID of a specific object of another class.…”

Section: Object Data Modelingmentioning

confidence: 99%

“…In addition, based on a well-defined data model, a data mining model, at the system level, represents the high-level output of data mining that is achieved by the corresponding data mining modeling processes. In the data mining reference model recommended by [Chapman et al, 2000], data modeling is a sub-step of the data understanding and preparation processes, while data mining modeling is called modeling without ambiguity.…”

Section: Introduction 11relational and Object Data Modelingmentioning

confidence: 99%

Probabilistic relational data mining

Sen¹

2010

Proceedings of the International Conference and Workshop on Emerging Trends in Technology

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Probabilistic Relational Data Mining (PRDM) refers to the use of Knowledge discovery in database (KDD) methods of learning probabilistic statistical models from relational data that has information about several types of objects. This is usually the case when the database has more than one table. PRDM provides techniques for discovering descriptive models, including relationships, correlations and causal dependencies, embedded in a set of objects as well as their component attributes. In essence, it is a marriage of probabilistic modeling, multi-relational database modeling, and object oriented modeling. The three modeling processes are integrated together into a data mining system to fulfill the overall modeling task, in which, intuitively speaking, database modeling plays a role of input, probabilistic modeling is like an output, and objectoriented modeling provides necessary background information.

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RAFTS: A T-38 Replacement for the 21st Century

Abstract: S"insights into tomorrow"?). A A LIS'.RIBUTION S'rATEMIENT A k Approved tke public feleajs o A..

Cited by 24 publications

References 0 publications

A New Drug-Shelf Arrangement for Reducing Medication Errors using Data Mining: A Case Study

A New Drug-Shelf Arrangement for Reducing Medication Errors using Data Mining: A Case Study

Data Mining in Clinical Data Sets: A Review

Probabilistic relational data mining

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