A bi-level interactive decision support framework to identify data mining-oriented electronic health record architectures

Zandi, Faramak

doi:10.1016/j.asoc.2014.01.001

Cited by 15 publications

(4 citation statements)

References 26 publications

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“…The results of the proposed T2F-AHP methodology are compared with the results gained from fuzzy simple additive weighting (SAW) method. SAW is used in many different application areas and generally used as an alternative comparison methodology: such as location selection [53], health record architectures identification [54], inventory model determination [55], network selection [56], outscore manufacturers evaluation [57] , strategic system selection [58]. The score of each alternative based on the fuzzy SAW method is calculated via Eq.…”

Section: Comparative Analysismentioning

confidence: 99%

Humanitarian Relief Supply Chain Performance Evaluation by a Scor Based Trapezoidal Type-2 Fuzzy Multi Criteria Decision Making Methodology: An Application in Turkey

Ayyıldız

Gümüş

2020

Scientia Iranica

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Determining the most important criteria to evaluate Humanitarian Relief Supply Chain (HRSC) performance is aimed in this study. For this purpose, Supply Chain Operations Reference (SCOR) model is adapted to HRSC. A trapezoidal type-2 fuzzy Analytic Hierarchy Process (T2F-AHP) methodology is proposed to evaluate the criteria influencing the performance of the HRSC, and also evaluate different Non-Governmental Organizations' performances operating in Turkey as a real case study. Results of this methodology is aimed to be used by both governmental and non-governmental organizations to improve their HRSC strategies. Lastly, results are discussed with sensitivity analysis to demonstrate the feasibility and applicability of the proposed methodology.

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Section: Comparative Analysismentioning

confidence: 99%

Humanitarian Relief Supply Chain Performance Evaluation by a Scor Based Trapezoidal Type-2 Fuzzy Multi Criteria Decision Making Methodology: An Application in Turkey

Ayyıldız

Gümüş

2020

Scientia Iranica

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“…Most studies which have been conducted around this issue focus on business intelligent (BI) (Azma & Mostafapour, 2012;Nofal & Yusof, 2013;Popovic et al, 2012), data mining (Aghdaie et al, 2014;Weerdt et al, 2013;Zandi, 2014), data linkage (Christen, 2008;Durham et al, 2012;Ferrante & Boyd, 2012) and knowledge discovery in databases (KDD) (Cheng et al, 2012;Lin et al, 2008). Even though these studies focus on the decision-making but they do not focus on the interaction between organizational data and organizational goals.…”

Section: Problem Scopementioning

confidence: 99%

Ontology Application for the Organizational Goals

Izhar¹,

Shoid²

2020

IJHRS

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Data is important in assisting decision-making in relation to the organizational goals. However, the trustworthiness of organizational data in relation to achieving the organizational goals is often questioned because of the vast amount of organizational data available. This paper advances the understanding of the organizational goals model based on ontology. This refers to the importance of assisting the organization to utilize relevant organizational data for decision-making in relation to the organizational goals. Therefore, domain experts and entrepreneurs can make a decision to what extend the organizational goals are achieved. The results show that ontology supports the relationship between the organizational goal elements as an effort to measure organizational data in relation to the organizational goals.

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“…The bi-level Interactive Simple Additive Weighting Model was then use to help medical decision makers gain a consensus on a DM-oriented EHR architecture. Bashir et al [14] proposed the effectiveness of an ensemble classifier for computer-aided breast cancer diagnosis. A novel combination of five heterogeneous classifiers, namely Naïve Bayes, Decision tree using Gini Index, Decision Tree using information gain, Support Vector Machine, and Memory-based Learner were used to make the ensemble framework.…”

Section: Review Of the Litreturementioning

confidence: 99%

Development of a Decision Support System for Handling Health Insurance Deduction

Khademolqorani¹,

Hamadani²

2015

ijacsa

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Abstract-Effective hospital management involves such activities as monitoring the flow of medication, controlling treatment, and billing for the patient's treatment. A major challenge between insurance companies and hospitals lies in the way medical treatment expenses for insured patients are reimbursed. In some cases, the insurance deduction leads to the loss of revenues by hospitals. This paper proposes a framework for the handling insurance deduction that integrates three major methodologies: Decision Support Systems, Data Mining, and Multiple Criteria Decision Making. To exemplify the practical utility of the framework, it is used to study hospital services and insurance deductions are extracted from 200,000 documents in 150 hospitals in Iran. To classify the kinds of services, decision trees are developed to mine hidden rules in the data which are then modified on the basis of some performance measures. The rules are then extracted and ranked using the TOPSIS method. The results show that the proposed framework is capable of effectively providing objective and comprehensive assessments of insurance deductions.

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A bi-level interactive decision support framework to identify data mining-oriented electronic health record architectures

Cited by 15 publications

References 26 publications

Humanitarian Relief Supply Chain Performance Evaluation by a Scor Based Trapezoidal Type-2 Fuzzy Multi Criteria Decision Making Methodology: An Application in Turkey

Humanitarian Relief Supply Chain Performance Evaluation by a Scor Based Trapezoidal Type-2 Fuzzy Multi Criteria Decision Making Methodology: An Application in Turkey

Ontology Application for the Organizational Goals

Development of a Decision Support System for Handling Health Insurance Deduction

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