Journal ArticleDOI
Healthcare information systems: data mining methods in the creation of a clinical recommender system
Lian Duan,W.N. Street,Eric Xu +2 more
TLDR
The proposed system uses correlations among nursing diagnoses, outcomes and interventions to create a recommender system for constructing nursing care plans, and utilises a prefix-tree structure common in itemset mining to construct a ranked list of suggested care plan items based on previously-entered items.Abstract:
Recommender systems have been extensively studied to present items, such as movies, music and books that are likely of interest to the user. Researchers have indicated that integrated medical information systems are becoming an essential part of the modern healthcare systems. Such systems have evolved to an integrated enterprise-wide system. In particular, such systems are considered as a type of enterprise information systems or ERP system addressing healthcare industry sector needs. As part of efforts, nursing care plan recommender systems can provide clinical decision support, nursing education, clinical quality control, and serve as a complement to existing practice guidelines. We propose to use correlations among nursing diagnoses, outcomes and interventions to create a recommender system for constructing nursing care plans. In the current study, we used nursing diagnosis data to develop the methodology. Our system utilises a prefix-tree structure common in itemset mining to construct a ranked list of suggested care plan items based on previously-entered items. Unlike common commercial systems, our system makes sequential recommendations based on user interaction, modifying a ranked list of suggested items at each step in care plan construction. We rank items based on traditional association-rule measures such as support and confidence, as well as a novel measure that anticipates which selections might improve the quality of future rankings. Since the multi-step nature of our recommendations presents problems for traditional evaluation measures, we also present a new evaluation method based on average ranking position and use it to test the effectiveness of different recommendation strategies.read more
Citations
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Journal ArticleDOI
Social media competitive analysis and text mining: A case study in the pizza industry
Wu He,Shenghua Zha,Ling Li +2 more
TL;DR: An in-depth case study which applies text mining to analyze unstructured text content on Facebook and Twitter sites of the three largest pizza chains: Pizza Hut, Domino's Pizza and Papa John's Pizza reveals the value of social media competitive analysis and the power of text mining as an effective technique to extract business value from the vast amount of available social media data.
Journal ArticleDOI
Big Data and cloud computing: innovation opportunities and challenges
TL;DR: This review introduces future innovations and a research agenda for cloud computing supporting the transformation of the volume, velocity, variety and veracity into values of Big Data for local to global digital earth science and applications.
Journal ArticleDOI
Data mining for the Internet of Things: literature review and challenges
TL;DR: A systematic way to review data mining in knowledge view, technique view, and application view, including classification, clustering, association analysis, time series analysis and outlier analysis is given.
Journal ArticleDOI
A survey on Data Mining approaches for Healthcare
Divya Tomar,Sonali Agarwal +1 more
TL;DR: This survey explores the utility of various Data Mining techniques such as classification, clustering, association, regression in health domain and a brief introduction of these techniques and their advantages and disadvantages.
References
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Journal ArticleDOI
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