Special Issue "eHealth and Artificial Intelligence"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 January 2019)

Special Issue Editors

Guest Editor
Dr. Donato IMPEDOVO

Department of Computer Science, University of Bari, Bari, Italy
Website | E-Mail
Interests: Biometrics; Automatic Signature Verification; Artificial Intelligence; Pattern Recognition, Signal Processing
Guest Editor
Prof. Giuseppe PIRLO

Department of Computer Science, University of Bari, Bari, Italy
Website | E-Mail
Interests: biometrics; automatic signature verification; artificial intelligence; pattern recognition; signal processing

Special Issue Information

Dear Colleagues,

Artificial Intelligence is changing the healthcare industry from many perspectives: Diagnosis, treatment and follow up. A major topic of AI in medicine is the that related to Clinical Decision Support (CDS) to assist clinicians at point of care.

CDS can be knowledge-based, where the AI areas involved are inference and logics and non-knowledge-based, where machine learning is used. CDS can support all aspects of clinical tasks, but, to be effective, it must be properly integrated within the clinical workflow, as well as with health records. A typical application is a Computer Aided Diagnosis (CAD) to assist doctors in the interpretation of medical images. CAD involves, not only AI, but also Computer Vision, Signal Processing and specific medical aspects. CADs find application in breast cancer, lung cancer, colon cancer, coronary artery disease, Alzheimer’s disease and many others.

Given the above, proposer of this Special Issue strongly believe the topic of eHealth is relevant to the AI community; in fact, many open research aspects are still open in the field from an AI perspective.

Prof. Donato Impedovo
Prof. Giuseppe Pirlo
Guest Editors

Manuscript Submission Information

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Keywords

  • Applications of AI in Health Care
  • Knowledge Management of Medical Data
  • Data Mining and Knowledge Discovery in Medicine
  • Medical Expert Systems
  • Personal medical feature data
  • Medical device technologies
  • Diagnoses and Therapy Support Systems
  • Machine Learning-based Medical Systems
  • Pattern Recognition in Medicine
  • Ambient Intelligence and Pervasive Computing in Medicine and Health Care
  • Brain-computer interfaces
  • VR/AR in medical education, diagnosis and surgery

Published Papers (14 papers)

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Editorial

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Open AccessEditorial eHealth and Artificial Intelligence
Information 2019, 10(3), 117; https://doi.org/10.3390/info10030117
Received: 18 March 2019 / Accepted: 18 March 2019 / Published: 19 March 2019
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Abstract
Artificial intelligence is changing the healthcare industry from many perspectives: diagnosis, treatment, and follow-up. A wide range of techniques has been proposed in the literature. In this special issue, 13 selected and peer-reviewed original research articles contribute to the application of artificial intelligence [...] Read more.
Artificial intelligence is changing the healthcare industry from many perspectives: diagnosis, treatment, and follow-up. A wide range of techniques has been proposed in the literature. In this special issue, 13 selected and peer-reviewed original research articles contribute to the application of artificial intelligence (AI) approaches in various real-world problems. Papers refer to the following main areas of interest: feature selection, high dimensionality, and statistical approaches; heart and cardiovascular diseases; expert systems and e-health platforms. Full article
(This article belongs to the Special Issue eHealth and Artificial Intelligence)

Research

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Open AccessArticle An Experimental Comparison of Feature-Selection and Classification Methods for Microarray Datasets
Information 2019, 10(3), 109; https://doi.org/10.3390/info10030109
Received: 29 January 2019 / Revised: 3 March 2019 / Accepted: 5 March 2019 / Published: 10 March 2019
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Abstract
In the last decade, there has been a growing scientific interest in the analysis of DNA microarray datasets, which have been widely used in basic and translational cancer research. The application fields include both the identification of oncological subjects, separating them from the [...] Read more.
In the last decade, there has been a growing scientific interest in the analysis of DNA microarray datasets, which have been widely used in basic and translational cancer research. The application fields include both the identification of oncological subjects, separating them from the healthy ones, and the classification of different types of cancer. Since DNA microarray experiments typically generate a very large number of features for a limited number of patients, the classification task is very complex and typically requires the application of a feature-selection process to reduce the complexity of the feature space and to identify a subset of distinctive features. In this framework, there are no standard state-of-the-art results generally accepted by the scientific community and, therefore, it is difficult to decide which approach to use for obtaining satisfactory results in the general case. Based on these considerations, the aim of the present work is to provide a large experimental comparison for evaluating the effect of the feature-selection process applied to different classification schemes. For comparison purposes, we considered both ranking-based feature-selection techniques and state-of-the-art feature-selection methods. The experiments provide a broad overview of the results obtainable on standard microarray datasets with different characteristics in terms of both the number of features and the number of patients. Full article
(This article belongs to the Special Issue eHealth and Artificial Intelligence)
Open AccessFeature PaperArticle Glomerular Filtration Rate Estimation by a Novel Numerical Binning-Less Isotonic Statistical Bivariate Numerical Modeling Method
Information 2019, 10(3), 100; https://doi.org/10.3390/info10030100
Received: 25 February 2019 / Accepted: 1 March 2019 / Published: 6 March 2019
Cited by 1 | PDF Full-text (701 KB) | HTML Full-text | XML Full-text
Abstract
Statistical bivariate numerical modeling is a method to infer an empirical relationship between unpaired sets of data based on statistical distributions matching. In the present paper, a novel efficient numerical algorithm is proposed to perform bivariate numerical modeling. The algorithm is then applied [...] Read more.
Statistical bivariate numerical modeling is a method to infer an empirical relationship between unpaired sets of data based on statistical distributions matching. In the present paper, a novel efficient numerical algorithm is proposed to perform bivariate numerical modeling. The algorithm is then applied to correlate glomerular filtration rate to serum creatinine concentration. Glomerular filtration rate is adopted in clinical nephrology as an indicator of kidney function and is relevant for assessing progression of renal disease. As direct measurement of glomerular filtration rate is highly impractical, there is considerable interest in developing numerical algorithms to estimate glomerular filtration rate from parameters which are easier to obtain, such as demographic and ‘bedside’ assays data. Full article
(This article belongs to the Special Issue eHealth and Artificial Intelligence)
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Open AccessArticle A Modified Robust FCM Model with Spatial Constraints for Brain MR Image Segmentation
Information 2019, 10(2), 74; https://doi.org/10.3390/info10020074
Received: 19 January 2019 / Revised: 13 February 2019 / Accepted: 19 February 2019 / Published: 21 February 2019
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Abstract
In brain magnetic resonance (MR) images, image quality is often degraded due to the influence of noise and outliers, which brings some difficulties for doctors to segment and extract brain tissue accurately. In this paper, a modified robust fuzzy c-means (MRFCM) algorithm for [...] Read more.
In brain magnetic resonance (MR) images, image quality is often degraded due to the influence of noise and outliers, which brings some difficulties for doctors to segment and extract brain tissue accurately. In this paper, a modified robust fuzzy c-means (MRFCM) algorithm for brain MR image segmentation is proposed. According to the gray level information of the pixels in the local neighborhood, the deviation values of each adjacent pixel are calculated in kernel space based on their median value, and the normalized adaptive weighted measure of each pixel is obtained. Both impulse noise and Gaussian noise in the image can be effectively suppressed, and the detail and edge information of the brain MR image can be better preserved. At the same time, the gray histogram is used to replace single pixel during the clustering process. The results of segmentation of MRFCM are compared with the state-of-the-art algorithms based on fuzzy clustering, and the proposed algorithm has the stronger anti-noise property, better robustness to various noises and higher segmentation accuracy. Full article
(This article belongs to the Special Issue eHealth and Artificial Intelligence)
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Open AccessArticle Noisy ECG Signal Analysis for Automatic Peak Detection
Information 2019, 10(2), 35; https://doi.org/10.3390/info10020035
Received: 29 October 2018 / Revised: 17 January 2019 / Accepted: 18 January 2019 / Published: 22 January 2019
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Abstract
Cardiac signal processing is usually a computationally demanding task as signals are heavily contaminated by noise and other artifacts. In this paper, an effective approach for peak point detection and localization in noisy electrocardiogram (ECG) signals is presented. Six stages characterize the implemented [...] Read more.
Cardiac signal processing is usually a computationally demanding task as signals are heavily contaminated by noise and other artifacts. In this paper, an effective approach for peak point detection and localization in noisy electrocardiogram (ECG) signals is presented. Six stages characterize the implemented method, which adopts the Hilbert transform and a thresholding technique for the detection of zones inside the ECG signal which could contain a peak. Subsequently, the identified zones are analyzed using the wavelet transform for R point detection and localization. The conceived signal processing technique has been evaluated, adopting ECG signals belonging to MIT-BIH Noise Stress Test Database, which includes specially selected Holter recordings characterized by baseline wander, muscle artifacts and electrode motion artifacts as noise sources. The experimental results show that the proposed method reaches most satisfactory performance, even when challenging ECG signals are adopted. The results obtained are presented, discussed and compared with some other R wave detection algorithms indicated in literature, which adopt the same database as a test bench. In particular, for a signal to noise ratio (SNR) equal to 6 dB, results with minimal interference from noise and artifacts have been obtained, since Se e +P achieve values of 98.13% and 96.91, respectively. Full article
(This article belongs to the Special Issue eHealth and Artificial Intelligence)
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Open AccessArticle HOLMeS: eHealth in the Big Data and Deep Learning Era
Information 2019, 10(2), 34; https://doi.org/10.3390/info10020034
Received: 12 November 2018 / Revised: 7 January 2019 / Accepted: 9 January 2019 / Published: 22 January 2019
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Abstract
Now, data collection and analysis are becoming more and more important in a variety of application domains, as long as novel technologies advance. At the same time, we are experiencing a growing need for human–machine interaction with expert systems, pushing research toward new [...] Read more.
Now, data collection and analysis are becoming more and more important in a variety of application domains, as long as novel technologies advance. At the same time, we are experiencing a growing need for human–machine interaction with expert systems, pushing research toward new knowledge representation models and interaction paradigms. In particular, in the last few years, eHealth—which usually indicates all the healthcare practices supported by electronic elaboration and remote communications—calls for the availability of a smart environment and big computational resources able to offer more and more advanced analytics and new human–computer interaction paradigms. The aim of this paper is to introduce the HOLMeS (health online medical suggestions) system: A particular big data platform aiming at supporting several eHealth applications. As its main novelty/functionality, HOLMeS exploits a machine learning algorithm, deployed on a cluster-computing environment, in order to provide medical suggestions via both chat-bot and web-app modules, especially for prevention aims. The chat-bot, opportunely trained by leveraging a deep learning approach, helps to overcome the limitations of a cold interaction between users and software, exhibiting a more human-like behavior. The obtained results demonstrate the effectiveness of the machine learning algorithms, showing an area under ROC (receiver operating characteristic) curve (AUC) of 74.65% when some first-level features are used to assess the occurrence of different chronic diseases within specific prevention pathways. When disease-specific features are added, HOLMeS shows an AUC of 86.78%, achieving a greater effectiveness in supporting clinical decisions. Full article
(This article belongs to the Special Issue eHealth and Artificial Intelligence)
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Open AccessFeature PaperArticle Automatic Diagnosis of Neurodegenerative Diseases: An Evolutionary Approach for Facing the Interpretability Problem
Information 2019, 10(1), 30; https://doi.org/10.3390/info10010030
Received: 11 December 2018 / Revised: 13 January 2019 / Accepted: 14 January 2019 / Published: 17 January 2019
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Abstract
Background: The use of Artificial Intelligence (AI) systems for automatic diagnoses is increasingly in the clinical field, being a useful support for the identification of several diseases. Nonetheless, the acceptance of AI-based diagnoses by the physicians is hampered by the black-box approach implemented [...] Read more.
Background: The use of Artificial Intelligence (AI) systems for automatic diagnoses is increasingly in the clinical field, being a useful support for the identification of several diseases. Nonetheless, the acceptance of AI-based diagnoses by the physicians is hampered by the black-box approach implemented by most performing systems, which do not clearly state the classification rules adopted. Methods: In this framework we propose a classification method based on a Cartesian Genetic Programming (CGP) approach, which allows for the automatic identification of the presence of the disease, and concurrently, provides the explicit classification model used by the system. Results: The proposed approach has been evaluated on the publicly available HandPD dataset, which contains handwriting samples drawn by Parkinson’s disease patients and healthy controls. We show that our approach compares favorably with state-of-the-art methods, and more importantly, allows the physician to identify an explicit model relevant for the diagnosis based on the most informative subset of features. Conclusion: The obtained results suggest that the proposed approach is particularly appealing in that, starting from the explicit model, it allows the physicians to derive a set of guidelines for defining novel testing protocols and intervention strategies. Full article
(This article belongs to the Special Issue eHealth and Artificial Intelligence)
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Open AccessArticle Contact-Less Real-Time Monitoring of Cardiovascular Risk Using Video Imaging and Fuzzy Inference Rules
Information 2019, 10(1), 9; https://doi.org/10.3390/info10010009
Received: 30 October 2018 / Revised: 12 December 2018 / Accepted: 24 December 2018 / Published: 29 December 2018
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Abstract
Conventional methods for measuring cardiovascular parameters use skin contact techniques requiring a measuring device to be worn by the user. To avoid discomfort of contact devices, camera-based techniques using photoplethysmography have been recently introduced. Nevertheless, these solutions are typically expensive and difficult to [...] Read more.
Conventional methods for measuring cardiovascular parameters use skin contact techniques requiring a measuring device to be worn by the user. To avoid discomfort of contact devices, camera-based techniques using photoplethysmography have been recently introduced. Nevertheless, these solutions are typically expensive and difficult to be used daily at home. In this work, we propose an innovative solution for monitoring cardiovascular parameters that is low cost and can be easily integrated within any common home environment. The proposed system is a contact-less device composed of a see-through mirror equipped with a camera that detects the person’s face and processes video frames using photoplethysmography in order to estimate the heart rate, the breath rate and the blood oxygen saturation. In addition, the color of lips is automatically detected via clustering-based color quantization. The estimated parameters are used to predict a risk of cardiovascular disease by means of fuzzy inference rules integrated in the mirror-based monitoring system. Comparing our system to a contact device in measuring vital parameters on still or slightly moving subjects, we achieve measurement errors that are within acceptable margins according to the literature. Moreover, in most cases, the response of the fuzzy rule-based system is comparable with that of the clinician in assessing a risk level of cardiovascular disease. Full article
(This article belongs to the Special Issue eHealth and Artificial Intelligence)
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Open AccessArticle Evaluation of a Rehabilitation System for the Elderly in a Day Care Center
Information 2019, 10(1), 3; https://doi.org/10.3390/info10010003
Received: 31 October 2018 / Revised: 15 December 2018 / Accepted: 15 December 2018 / Published: 22 December 2018
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Abstract
This paper presents a rehabilitation system based on a customizable exergame protocol to prevent falls in the elderly population. The system is based on depth sensors and exergames. The experiments carried out with several seniors, in a day care center, make it possible [...] Read more.
This paper presents a rehabilitation system based on a customizable exergame protocol to prevent falls in the elderly population. The system is based on depth sensors and exergames. The experiments carried out with several seniors, in a day care center, make it possible to evaluate the usability and the efficiency of the system. The outcomes highlight the user-friendliness, the very good usability of the developed system and the significant enhancement of the elderly in maintaining a physical activity. The performance of the postural response is improved by an average of 80%. Full article
(This article belongs to the Special Issue eHealth and Artificial Intelligence)
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Open AccessArticle A Mobile Acquisition System and a Method for Hips Sway Fluency Assessment
Information 2018, 9(12), 321; https://doi.org/10.3390/info9120321
Received: 31 October 2018 / Revised: 8 December 2018 / Accepted: 10 December 2018 / Published: 12 December 2018
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Abstract
The present contribution focuses on the estimation of the Cartesian kinematic jerk of the hips’ orientation during a full three-dimensional movement in the context of enabling eHealth applications of advanced mathematical signal analysis. The kinematic jerk index is estimated on the basis of [...] Read more.
The present contribution focuses on the estimation of the Cartesian kinematic jerk of the hips’ orientation during a full three-dimensional movement in the context of enabling eHealth applications of advanced mathematical signal analysis. The kinematic jerk index is estimated on the basis of gyroscopic signals acquired offline through a smartphone. A specific free mobile application is used to acquire the gyroscopic signals and to transmit them to a personal computer through a wireless network. The personal computer elaborates the acquired data and returns the kinematic jerk index associated with a motor task. A comparison of the kinematic jerk index value on a number of data sets confirms that such index can be used to evaluate the fluency of hips orientation during motion. The present research confirms that the proposed gyroscopic data acquisition/processing setup constitutes an inexpensive and portable solution to motion fluency analysis. The proposed data-acquisition and data-processing setup may serve as a supporting eHealth technology in clinical bio-mechanics as well as in sports science. Full article
(This article belongs to the Special Issue eHealth and Artificial Intelligence)
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Open AccessArticle LICIC: Less Important Components for Imbalanced Multiclass Classification
Information 2018, 9(12), 317; https://doi.org/10.3390/info9120317
Received: 22 October 2018 / Revised: 19 November 2018 / Accepted: 6 December 2018 / Published: 9 December 2018
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Abstract
Multiclass classification in cancer diagnostics, using DNA or Gene Expression Signatures, but also classification of bacteria species fingerprints in MALDI-TOF mass spectrometry data, is challenging because of imbalanced data and the high number of dimensions with respect to the number of instances. In [...] Read more.
Multiclass classification in cancer diagnostics, using DNA or Gene Expression Signatures, but also classification of bacteria species fingerprints in MALDI-TOF mass spectrometry data, is challenging because of imbalanced data and the high number of dimensions with respect to the number of instances. In this study, a new oversampling technique called LICIC will be presented as a valuable instrument in countering both class imbalance, and the famous “curse of dimensionality” problem. The method enables preservation of non-linearities within the dataset, while creating new instances without adding noise. The method will be compared with other oversampling methods, such as Random Oversampling, SMOTE, Borderline-SMOTE, and ADASYN. F1 scores show the validity of this new technique when used with imbalanced, multiclass, and high-dimensional datasets. Full article
(This article belongs to the Special Issue eHealth and Artificial Intelligence)
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Open AccessArticle Towards Expert-Based Speed–Precision Control in Early Simulator Training for Novice Surgeons
Information 2018, 9(12), 316; https://doi.org/10.3390/info9120316
Received: 14 October 2018 / Revised: 1 December 2018 / Accepted: 5 December 2018 / Published: 9 December 2018
Cited by 1 | PDF Full-text (2442 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Simulator training for image-guided surgical interventions would benefit from intelligent systems that detect the evolution of task performance, and take control of individual speed–precision strategies by providing effective automatic performance feedback. At the earliest training stages, novices frequently focus on getting faster at [...] Read more.
Simulator training for image-guided surgical interventions would benefit from intelligent systems that detect the evolution of task performance, and take control of individual speed–precision strategies by providing effective automatic performance feedback. At the earliest training stages, novices frequently focus on getting faster at the task. This may, as shown here, compromise the evolution of their precision scores, sometimes irreparably, if it is not controlled for as early as possible. Artificial intelligence could help make sure that a trainee reaches her/his optimal individual speed–accuracy trade-off by monitoring individual performance criteria, detecting critical trends at any given moment in time, and alerting the trainee as early as necessary when to slow down and focus on precision, or when to focus on getting faster. It is suggested that, for effective benchmarking, individual training statistics of novices are compared with the statistics of an expert surgeon. The speed–accuracy functions of novices trained in a large number of experimental sessions reveal differences in individual speed–precision strategies, and clarify why such strategies should be automatically detected and controlled for before further training on specific surgical task models, or clinical models, may be envisaged. How expert benchmark statistics may be exploited for automatic performance control is explained. Full article
(This article belongs to the Special Issue eHealth and Artificial Intelligence)
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Open AccessArticle Dynamic Handwriting Analysis for Supporting Earlier Parkinson’s Disease Diagnosis
Information 2018, 9(10), 247; https://doi.org/10.3390/info9100247
Received: 15 September 2018 / Revised: 25 September 2018 / Accepted: 28 September 2018 / Published: 3 October 2018
Cited by 2 | PDF Full-text (283 KB) | HTML Full-text | XML Full-text
Abstract
Machine learning techniques are tailored to build intelligent systems to support clinicians at the point of care. In particular, they can complement standard clinical evaluations for the assessment of early signs and manifestations of Parkinson’s disease (PD). Patients suffering from PD typically exhibit [...] Read more.
Machine learning techniques are tailored to build intelligent systems to support clinicians at the point of care. In particular, they can complement standard clinical evaluations for the assessment of early signs and manifestations of Parkinson’s disease (PD). Patients suffering from PD typically exhibit impairments of previously learned motor skills, such as handwriting. Therefore, handwriting can be considered a powerful marker to develop automatized diagnostic tools. In this paper, we investigated if and to which extent dynamic features of the handwriting process can support PD diagnosis at earlier stages. To this end, a subset of the publicly available PaHaW dataset has been used, including those patients showing only early to mild degree of disease severity. We developed a classification framework based on different classifiers and an ensemble scheme. Some encouraging results have been obtained; in particular, good specificity performances have been observed. This indicates that a handwriting-based decision support tool could be used to administer screening tests useful for ruling in disease. Full article
(This article belongs to the Special Issue eHealth and Artificial Intelligence)
Open AccessArticle A Study of a Health Resources Management Platform Integrating Neural Networks and DSS Telemedicine for Homecare Assistance
Information 2018, 9(7), 176; https://doi.org/10.3390/info9070176
Received: 20 June 2018 / Revised: 6 July 2018 / Accepted: 6 July 2018 / Published: 19 July 2018
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Abstract
The proposed paper is related to a case of study of an e-health telemedicine system oriented on homecare assistance and suitable for de-hospitalization processes. The proposed platform is able to transfer efficiently the patient analyses from home to a control room of a [...] Read more.
The proposed paper is related to a case of study of an e-health telemedicine system oriented on homecare assistance and suitable for de-hospitalization processes. The proposed platform is able to transfer efficiently the patient analyses from home to a control room of a clinic, thus potentially reducing costs and providing high-quality assistance services. The goal is to propose an innovative resources management platform (RMP) integrating an innovative homecare decision support system (DSS) based on a multilayer perceptron (MLP) artificial neural network (ANN). The study is oriented in predictive diagnostics by proposing an RMP integrating a KNIME (Konstanz Information Miner) MLP-ANN workflow experimented on blood pressure systolic values. The workflow elaborates real data transmitted via the cloud by medical smart sensors and provides a prediction of the patient status. The innovative RMP-DSS is then structured to enable three main control levels. The first one is a real-time alerting condition triggered when real-time values exceed a threshold. The second one concerns preventative action based on the analysis of historical patient data, and the third one involves alerting due to patient status prediction. The proposed study combines the management of processes with DSS outputs, thus optimizing the homecare assistance activities. Full article
(This article belongs to the Special Issue eHealth and Artificial Intelligence)
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