Infection prediction using physiological and social data in social environments

Baldominos, Alejandro; Ogul, Hasan; Colomo‐Palacios, Ricardo; Sanz-Moreno, José; Pulido, José Manuel Gómez

doi:10.1016/j.ipm.2020.102213

Cited by 7 publications

(21 citation statements)

References 30 publications

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“…The novelty of our proposal lies in the use of microservices in clinical forecast scenarios (infectious diseases), as we propose a continuous construction of an online database to obtain predictive models for the detection of infectious diseases in elderly patients, inspired by the SPIDEP project [13]. A recommender system [6] is also used to support remote assistance in interpreting changes in the vital signs of institutionalised people and in triggering early alerts in the case of possible infection.…”

Section: B New Aspects Of the Architecturementioning

confidence: 99%

“…A project called "Design and implementation of a low-cost intelligent system for the pre-diagnosis and telecare of infectious diseases in elderly people (SPIDEP)" [13] was carried out by a consortium of Latin American and European R&D entities, the aim of which was to build an intelligent system based on information technologies (ICT) to support the early diagnosis of infectious diseases [14] by integrating a machine learning-based inference system to improve decision support in the prevention, treatment and management of infectious diseases [6], [15].…”

Section: Introductionmentioning

confidence: 99%

“…It should be noted that this study aims to contribute to increasing knowledge in the pre-diagnosis of infectious diseases and offer an alternative vision of telemonitoring of elderly people, with the support of ICTs, by the medical team [5], [6], [9]. This present work aims to incorporate and improve the results obtained from the previous studies that have achieve several significant advances for the medical and health care team, which are: the process of taking measurements is very fast, managing to collect and send the measurement data in less than 2 minutes.…”

Section: Introductionmentioning

confidence: 99%

“…This present work aims to incorporate and improve the results obtained from the previous studies that have achieve several significant advances for the medical and health care team, which are: the process of taking measurements is very fast, managing to collect and send the measurement data in less than 2 minutes. These features are essential for the wellness of the resident, who perceives the personal care without discomfort [9] and also the quality of health care grows as well as the satisfaction of residents, relatives and nursing personnel [6]. Another important advantage of the speed of the measurement process is that it increases the productivity of the nursing staff, increasing the residents/elderly assistant ratio [5].…”

Section: Introductionmentioning

confidence: 99%

“…Another important advantage of the speed of the measurement process is that it increases the productivity of the nursing staff, increasing the residents/elderly assistant ratio [5]. In addition, this benefits the patient directly, since pre-diagnosis through a Clinical Decision Support System (CDSS), supported by telemonitoring, allows the detection of the infection in an initial state and can anticipate the timely administration of corresponding medical treatment, thus avoiding the aggressive spread of the infection afflicting the patient [6], [14].…”

Section: Introductionmentioning

confidence: 99%

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Telemonitoring System for Infectious Disease Prediction in Elderly People Based on a Novel Microservice Architecture

et al. 2020

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This article describes the design, development and implementation of a set of microservices based on an architecture that enables detection and assisted clinical diagnosis within the field of infectious diseases of elderly patients, via a telemonitoring system. The proposed system is designed to continuously update a medical database fed with vital signs from biosensor kits applied by nurses to elderly people on a daily basis. The database is hosted in the cloud and is managed by a flexible microservices software architecture. The computational paradigms of the edge and the cloud were used in the implementation of a hybrid cloud architecture in order to support versatile high-performance applications under the microservices pattern for the pre-diagnosis of infectious diseases in elderly patients. The results of an analysis of the usability of the equipment, the performance of the architecture and the service concept show that the proposed e-health system is feasible and innovative. The system components are also selected to give a cost-effective implementation for people living in disadvantaged areas. The proposed e-health system is also suitable for distributed computing, big data and NoSQL structures, thus allowing the immediate application of machine learning and AI algorithms to discover knowledge patterns from the overall population.

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Section: B New Aspects Of the Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Telemonitoring System for Infectious Disease Prediction in Elderly People Based on a Novel Microservice Architecture

et al. 2020

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Predictive health monitoring: Leveraging artificial intelligence for early detection of infectious diseases in nursing home residents through discontinuous vital signs analysis

Garcés-Jiménez,

Polo-Luque,

Gómez-Pulido

et al. 2024

Computers in Biology and Medicine

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The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review

Alfred¹,

Obit²

2021

Heliyon

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Machine learning (ML) methods can be leveraged to prevent the spread of deadly infectious disease outbreak (e.g., COVID-19). This can be done by applying machine learning methods in predicting and detecting the deadly infectious disease. Most reviews did not discuss about the machine learning algorithms, datasets and performance measurements used for various applications in predicting and detecting the deadly infectious disease. In contrast, this paper outlines the literature review based on two major ways (e.g., prediction, detection) to limit the spread of deadly disease outbreaks. Hence, this study aims to investigate the state of the art, challenges and future works of leveraging ML methods to detect and predict deadly disease outbreaks according to two categories mentioned earlier. Specifically, this study provides a review on various approaches (e.g., individual and ensemble models), types of datasets, parameters or variables and performance measures used in the previous works. The literature review included all articles from journals and conference proceedings published from 2010 through 2020 in Scopus indexed databases using the search terms Predicting Disease Outbreaks and/or Detecting Disease using Machine Learning . The findings from this review focus on commonly used machine learning approaches, challenges and future works to limit the spread of deadly disease outbreaks through preventions and detections.

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Infection prediction using physiological and social data in social environments

Cited by 7 publications

References 30 publications

Telemonitoring System for Infectious Disease Prediction in Elderly People Based on a Novel Microservice Architecture

Telemonitoring System for Infectious Disease Prediction in Elderly People Based on a Novel Microservice Architecture

Predictive health monitoring: Leveraging artificial intelligence for early detection of infectious diseases in nursing home residents through discontinuous vital signs analysis

The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review

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