Bone and Joint Institute
Document Type
Article
Publication Date
2020
Journal
IEEE ACCESS
Volume
8
First Page
217150
Last Page
217150
URL with Digital Object Identifier
10.1109/ACCESS.2020.3042342
Abstract
Smart city environments, when applied to healthcare, improve the quality of people's lives, enabling, for instance, disease prediction and treatment monitoring. In medical settings, case prioritization is of great importance, with beneficial outcomes both in terms of patient health and physicians' daily work. Recommender systems are an alternative to automatically integrate the data generated in such environments with predictive models and recommend actions, content, or services. The data produced by smart devices are accurate and reliable for predictive and decision-making contexts. This study main purpose is to assist patients and doctors in the early detection of disease or prediction of postoperative worsening through constant monitoring. To achieve this objective, this study proposes an architecture for recommender systems applied to healthcare, which can prioritize emergency cases. The architecture brings an ensemble approach for prediction, which adopts multiple Machine Learning algorithms. The methodology used to carry out the study followed three steps. First, a systematic literature mapping, second, the construction and development of the architecture, and third, the evaluation through two case studies. The results demonstrated the feasibility of the proposal. The predictions are promising and adherent to the application context for accurate datasets with a low amount of noises or missing values.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Notes
Copyright 2020, the authors. This article is published under a CC-BY Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/)
This article was published as:
F. Neves et al., "Heath-PRIOR: An Intelligent Ensemble Architecture to Identify Risk Cases in Healthcare," in IEEE Access, vol. 8, pp. 217150-217168, 2020, doi: 10.1109/ACCESS.2020.3042342.