Data Mining and Critical Success Factors in Data Mining Projects

Chen, Yun; Hu, Dengfeng; Zhang, Guozheng

doi:10.1007/0-387-34403-9_39

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…However, both these methods work best for dense datasets. Data mining has been used to extract knowledge in some such data sets, albeit with limited success (Chen, Hu, & Zhang, 2006). Typical EHR data, on the other hand, are highly sparse.…”

Section: Introductionmentioning

confidence: 99%

Framework for Mining and Analysis of Standardized Nursing Care Plan Data

et al. 2016

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Despite an unprecedented amount of health related data being amassed from various technological innovations, our ability to process this data and extract hidden knowledge has yet to catch up with this explosive growth. Although nursing care plans can be an effective tool to support the achievement of desired patient outcomes, their online collection, storage, and processing is lagging far behind. As a result, the impact of nursing care is not well understood from qualitative as well as quantitative perspectives. In this paper we first outline a complete life cycle of nursing care data, and present a knowledge discovery and analysis framework for such data sets. We also highlight big data issues pertaining to the analysis of nursing care data. Using an exemplar data set, we demonstrate the broad applicability of the proposed framework by showing knowledge discovery results for different outcomes related to patients, nursing staff, and administrators.

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Section: Introductionmentioning

confidence: 99%

Framework for Mining and Analysis of Standardized Nursing Care Plan Data

et al. 2016

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“…Require a predetermined hypothesis [91] and require parametric assumptions (e.g. homogeneity of variance).…”

Section: Data Miningmentioning

confidence: 99%

From data mining to wisdom mining

Khan

Shaheen

2021

Journal of Information Science

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The knowledge gained from data mining is highly dependent on the experience of an expert for further analysis to increase effectiveness and wise decision-making. This mined knowledge requires actionability enhancement before it can be applied to real-world problems. The literature highlights the reasons that emerged the need to incorporate human wisdom in decision-making for complex problems. To solve this problem, a domain called ‘Wisdom Mining’ is recommended, proposing a set of algorithms parallel to the algorithms proposed by the data mining. In wisdom mining, a process to extract wisdom needs to be defined with less influence from an expert. This review proposed improvements to data mining techniques and their applications in the real world and emphasised the need to seek ways to harness wisdom from data. This study covers the diverse definitions and different perspectives of wisdom within philosophy, psychology, management and computer science. This comprehensive literature review served as a foundation for constructing a wise decision framework that aided in identifying the wisdom factors like context, utility, location and time. The inclusion of these wisdom factors in existing data mining algorithms makes the transition from data mining to wisdom mining possible. This research includes the relationship between these two mining process that facilitated further elucidation of the wisdom mining process. Potential research trends in the domain are also seen as a potential endeavour to improve the analysis and use of data.

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“…Due to the presence of non-linear variables and their varying degree of importance in different domains, the problem is complex and extremely challenging. Different data mining techniques have been used to extract knowledge available in some of these data sets, albeit with limited success until now [2]. Various algorithms [3,4] have been introduced, that use row-wise enumeration method instead of traditional column-wise enumeration method to address the dimensionality problem, however, they have their own limitations as they work best for dense high dimensional datasets, with significantly lower number of rows compared to number of columns.…”

Section: Introductionmentioning

confidence: 99%

Predicting Hospital Re-Admissions from Nursing Care Data of Hospitalized Patients

Lodhi

Ansari

Yao

et al. 2017

Lecture Notes in Computer Science

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Readmission rates in the hospitals are increasingly being used as a benchmark to determine the quality of healthcare delivery to hospitalized patients. Around three-fourths of all hospital re-admissions can be avoided, saving billions of dollars. Many hospitals have now deployed electronic health record (EHR) systems that can be used to study issues that trigger readmission.However, most of the EHRs are high dimensional and sparsely populated, and analyzing such data sets is a Big Data challenge. The effect of some of the well-known dimension reduction techniques is minimized due to presence of non-linear variables. We use association mining as a dimension reduction method and the results are used to develop models, using data from an existing nursing EHR system, for predicting risk of re-admission to the hospitals. These models can help in determining effective treatments for patients to minimize the possibility of re-admission, bringing down the cost and increasing the quality of care provided to the patients. Results from the models show significantly accurate predictions of patient re-admission.

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Data Mining and Critical Success Factors in Data Mining Projects

Cited by 4 publications

References 4 publications

Framework for Mining and Analysis of Standardized Nursing Care Plan Data

Framework for Mining and Analysis of Standardized Nursing Care Plan Data

From data mining to wisdom mining

Predicting Hospital Re-Admissions from Nursing Care Data of Hospitalized Patients

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