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
More filters
Book ChapterDOI
Health Recommender System and Its Applicability with MapReduce Framework
TL;DR: This paper elaborates health recommender system (HRS) and gives a clear picture of how MapReduce Framework and Hadoop technology will help in improving the scalability and efficiency of HRS by stating illustrations.
Book ChapterDOI
Multipath Load Balancing and Secure Adaptive Routing Protocol for Service Oriented WSNs
TL;DR: A Secure Multipath AODV (SMAODV) protocol is put forth in which RSA algorithm is used for secure data transmission and path vacant ratio is calculated to discover the link disjoint path to destination sensor node from source sensor nodes from all presented paths in a network.
Proceedings ArticleDOI
Visualization of Passively Extracted HL7 Production Metrics
TL;DR: A system capable of displaying production metrics for healthcare facilities, by extracting HL7 messages and other eHealth relevant protocols directly from the institution´s network infrastructure, and able to populate a knowledge database with meaningful information derived from the gathered data is proposed.
Proceedings Article
A Hybrid Health Journey Recommender System Using Electronic Medical Records.
Soheil Jamshidi,MohamadAli Torkamani,Jynelle Mellen,Malhar Jhaveri,Penny Pan,James Chung,Hakan Kardes +6 more
TL;DR: This system employs an ensembling technique, where at its core, it has a Bayesian network that uses administrative claims data but could be extended to use Electronic Health Records data for learning the structure of the interwoven health graph.
Journal ArticleDOI
Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering
TL;DR: In this article, a hybrid dynamic and multi-collaborative filtering method was proposed to improve information retrieval from electronic health records, which leverages the key idea of collaborative filtering, originating from Recommender Systems, for prioritizing information based on various similarities among physicians, patients and information items.
References
More filters
Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
BookDOI
To Err Is Human Building a Safer Health System
TL;DR: Boken presenterer en helhetlig strategi for hvordan myndigheter, helsepersonell, industri og forbrukere kan redusere medisinske feil.
Journal ArticleDOI
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Journal ArticleDOI
Evaluating collaborative filtering recommender systems
TL;DR: The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.