Challenges and Opportunities of IoT and AI in Pneumology

Mongelli, Maurizio; Orani, Vanessa; Cambiaso, Enrico; Vaccari, Ivan; Paglialonga, Alessia; Braido, Fulvio; Catalano, Chiara Eva

doi:10.1109/dsd51259.2020.00054

Cited by 6 publications

(6 citation statements)

References 45 publications

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“…To achieve a fair level of monitoring of patients’ vital parameters, the Pneulytics framework aims to combine IoMT devices with innovative artificial intelligence algorithms with the aim of preventing and treating diseases, in this specific case COPD, through remote monitoring, performed directly by doctors and health entities. The Pneulytics prototype is composed of some hardware components used for the collection of patient parameters, obtained through IoMT devices, and a software part for the accurate processing of the data recovered from the sensors through machine learning algorithms, specifically explainable AI [ 5 ], combined with historical data. In the previous work, data were focused only on a smart inhaler, while in this work, we elaborated data retrieved by other IoMT as described in Section 3.1 .…”

Section: Pneulytics Frameworkmentioning

confidence: 99%

“…As each rule defines a specific stratification of data, the potential statistical significance should be assessed. The validation procedure follows [ 5 ] by exploiting the Fisher’s Exact Test (FET), which is suitable for a small amount of data.…”

Section: The Artificial Intelligence Approachmentioning

confidence: 99%

“…However, the following statements should be highlighted. First of all, the synthesis made on all the features is useful in front of several sources of information (as in [ 5 ]); in this case, the LLM automatically concentrates the attention on a quite small subset of features. This is captured by both feature ranking and value ranking, not shown here, which give a clear indication of the most significant feature and ranges of values for each feature in mapping the output classes.…”

Section: The Artificial Intelligence Approachmentioning

confidence: 99%

“…Within this umbrella, we propose a new implementation for remote monitoring through Pneulytics, a platform to monitor and manage patients with COPD [ 5 ]. Pneulytics can be used by health systems, clinical centers, pulmonology wards and clinics.…”

Section: Introductionmentioning

confidence: 99%

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A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data

Vaccari

Orani

Paglialonga

et al. 2021

Sensors

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The application of machine learning and artificial intelligence techniques in the medical world is growing, with a range of purposes: from the identification and prediction of possible diseases to patient monitoring and clinical decision support systems. Furthermore, the widespread use of remote monitoring medical devices, under the umbrella of the “Internet of Medical Things” (IoMT), has simplified the retrieval of patient information as they allow continuous monitoring and direct access to data by healthcare providers. However, due to possible issues in real-world settings, such as loss of connectivity, irregular use, misuse, or poor adherence to a monitoring program, the data collected might not be sufficient to implement accurate algorithms. For this reason, data augmentation techniques can be used to create synthetic datasets sufficiently large to train machine learning models. In this work, we apply the concept of generative adversarial networks (GANs) to perform a data augmentation from patient data obtained through IoMT sensors for Chronic Obstructive Pulmonary Disease (COPD) monitoring. We also apply an explainable AI algorithm to demonstrate the accuracy of the synthetic data by comparing it to the real data recorded by the sensors. The results obtained demonstrate how synthetic datasets created through a well-structured GAN are comparable with a real dataset, as validated by a novel approach based on machine learning.

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Section: Pneulytics Frameworkmentioning

confidence: 99%

Section: The Artificial Intelligence Approachmentioning

confidence: 99%

Section: The Artificial Intelligence Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data

Vaccari

Orani

Paglialonga

et al. 2021

Sensors

Self Cite

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“…Healthcare is a collaborative environment that involves many organizations including pharmaceuticals, hospitals, and insurance companies, and therefore multiple users/multiple actors with different roles are accessing the same resources. Access to a patient's health record is required for proper diagnosis and prescription [2], insurance claims and data analytics [3]. Since the patients' health data is sensitive in nature and requires proper management to avoid privacy breaches.…”

Section: Introductionmentioning

confidence: 99%

Granular Data Access Control with a Patient-Centric Policy Update for Healthcare

Khan

Tahir

et al. 2021

Sensors

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Healthcare is a multi-actor environment that requires independent actors to have a different view of the same data, hence leading to different access rights. Ciphertext Policy-Attribute-based Encryption (CP-ABE) provides a one-to-many access control mechanism by defining an attribute’s policy over ciphertext. Although, all users satisfying the policy are given access to the same data, this limits its usage in the provision of hierarchical access control and in situations where different users/actors need to have granular access of the data. Moreover, most of the existing CP-ABE schemes either provide static access control or in certain cases the policy update is computationally intensive involving all non-revoked users to actively participate. Aiming to tackle both the challenges, this paper proposes a patient-centric multi message CP-ABE scheme with efficient policy update. Firstly, a general overview of the system architecture implementing the proposed access control mechanism is presented. Thereafter, for enforcing access control a concrete cryptographic construction is proposed and implemented/tested over the physiological data gathered from a healthcare sensor: shimmer sensor. The experiment results reveal that the proposed construction has constant computational cost in both encryption and decryption operations and generates constant size ciphertext for both the original policy and its update parameters. Moreover, the scheme is proven to be selectively secure in the random oracle model under the q-Bilinear Diffie Hellman Exponent (q-BDHE) assumption. Performance analysis of the scheme depicts promising results for practical real-world healthcare applications.

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