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
Desenvolvimento de protótipo de chatbot para avaliação da maturação da fístula arteriovenosa
Andrea Lago da Silva,Bianca Rafaela Correia,Kênia Rocha Leite Zaccaro,Juliana Faria Campos,Jaqueline da Silva Soares Souto,Marcos Antônio Fernandes Brandão +5 more
Journal ArticleDOI
Mindcraft, a Mobile Mental Health Monitoring Platform for Children and Young People: Development and Acceptability Pilot Study
Balasundaram Kadirvelu,Xiaofei Hu,Victoria Burmester,B. Girela-Serrano,Julia Gledhill,Martina Di Simplicio,Dasha Nicholls,A. Aldo Faisal +7 more
TL;DR: Mindcraft as mentioned in this paper is a mobile mental health platform for children and young people, which integrates passive sensor data monitoring with active self-reported updates through an engaging user interface to monitor their well-being.
Proceedings ArticleDOI
Generating Textual Descriptions for Recommendation Results using Fuzzy Linguistic Summaries
TL;DR: A novel method of generating descriptive explanations of recom-mender systems result via fuzzy linguistic summaries is introduced and its pilot implementation for movies recommendation is being provided.
A literature review in data mining models used for survivability prediction of cancer patients
TL;DR: An overview of the current research being carried out using the data mining techniques for prognosis of cancers is presented, to identify the well-performing data mining algorithms used on medical databases in order to predict survivability of cancer patients.
Book ChapterDOI
A Mobile Recommender System for Location-Aware Telemedical Diagnostics
TL;DR: The aim is to complement the diagnoses made by physicians in rural hospitals of developing countries, in remote areas or in situations of uncertainty by machine recommendations that draw on large bases of expert knowledge to reduce the risk to patients.
References
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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.