Intelligent context-based healthcare metadata aggregator in internet of medical things platform

Alam, Mahbub Ul; Rahmani, Rahim

doi:10.1016/j.procs.2020.07.058

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…Effective data aggregation with the help of metadata aggregation [ 51 ], and medical sensors [ 52 ] can provide us with new insights. As the inter-relations of different medical conditions or diseases are ubiquitous phenomena among patients (comorbidity), incorporating this aspect (for example, general infection detection from various input sources and outcomes) could be an exciting option to explore further.…”

Section: Discussionmentioning

confidence: 99%

FedSepsis: A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano Devices

Alam

Rahmani

2023

Sensors

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The concept of the Internet of Medical Things brings a promising option to utilize various electronic health records stored in different medical devices and servers to create practical but secure clinical decision support systems. To achieve such a system, we need to focus on several aspects, most notably the usability aspect of deploying it using low-end devices. This study introduces one such application, namely FedSepsis, for the early detection of sepsis using electronic health records. We incorporate several cutting-edge deep learning techniques for the prediction and natural-language processing tasks. We also explore the multimodality aspect for the better use of electronic health records. A secure distributed machine learning mechanism is essential to building such a practical internet of medical things application. To address this, we analyze two federated learning techniques. Moreover, we use two different kinds of low-computational edge devices, namely Raspberry Pi and Jetson Nano, to address the challenges of using such a system in a practical setting and report the comparisons. We report several critical system-level information about the devices, namely CPU utilization, disk utilization, process CPU threads in use, process memory in use (non-swap), process memory available (non-swap), system memory utilization, temperature, and network traffic. We publish the prediction results with the evaluation metrics area under the receiver operating characteristic curve, the area under the precision–recall curve, and the earliness to predict sepsis in hours. Our results show that the performance is satisfactory, and with a moderate amount of devices, the federated learning setting results are similar to the single server-centric setting. Multimodality provides the best results compared to any single modality in the input features obtained from the electronic health records. Generative adversarial neural networks provide a clear superiority in handling the sparsity of electronic health records. Multimodality with the generative adversarial neural networks provides the best result: the area under the precision–recall curve is 96.55%, the area under the receiver operating characteristic curve is 99.35%, and earliness is 4.56 h. FedSepsis suggests that incorporating such a concept together with low-end computational devices could be beneficial for all the medical sector stakeholders and should be explored further.

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Section: Discussionmentioning

confidence: 99%

FedSepsis: A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano Devices

Alam

Rahmani

2023

Sensors

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“…As IoMT deals with the effective integration of the medical equipment and its associated data, a careful exploratory analysis of this heterogeneous data and their applicability should be investigated together. Concise and practical IoMT data aggregation framework [ 58 ] should be analyzed as well. Many medical conditions or diseases are interrelated (co-morbidity), and the diagnosis process is relatively sparse with different medical equipment and data.…”

Section: Discussionmentioning

confidence: 99%

Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application

Alam

Rahmani

2021

Sensors

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Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, Fscore for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain.

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“…Alam and Rahmani ( 2020 ) mentioned that the current IoMT data formats are inconsistent and distributed on devices with different access rights, presenting numerous challenges for effectively using healthcare data. IoMT information strategies enable the increased development speed of pandemic prevention technology platforms.…”

Section: Literature Reviewmentioning

confidence: 99%

National IoMT platform strategy portfolio decision model under the COVID-19 environment: based on the financial and non-financial value view

et al. 2022

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The Internet of Medical Things (IoMT) is an emerging technology in the healthcare revolution which provides real-time healthcare information communication and reasonable medical resource allocation. The COVID-19 pandemic has had a significant effect on people’s lives and has affected healthcare capacities. It is important for integrated IoMT platform development to overcome the global pandemic challenges. This study proposed the national IoMT platform strategy portfolio decision-making model from the non-financial (technology, organization, environment) and financial perspectives. As a solution to the decision problem, initially, the decision-making trial and evaluation laboratory (DEMATEL) technology were employed to capture the cause-effect relationship based on the perspectives and criteria obtained from the insight of an expert team. The analytic network process (ANP) and pairwise comparisons were then used to determine the weights for the strategy. Simultaneously, this study incorporated IoMT platform resource limitations into the zero–one goal programming (ZOGP) method to obtain an optimal portfolio selection for IoMT platform strategy planning. The results showed that the integrated MCDM method produced reasonable results for selecting the most appropriate IoMT platform strategy portfolio when considering resource constraints such as system installation costs, consultant fees, infrastructure costs, reduction of medical staff demand, and improvement rates for diagnosis efficiency. The decision-making model of the IoMT platform in this study was conclusive and significantly compelling to aid government decision makers in concentrating their efforts on planning IoMT strategies in response to various pandemic and medical resource allocations.

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Intelligent context-based healthcare metadata aggregator in internet of medical things platform

Cited by 5 publications

References 24 publications

FedSepsis: A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano Devices

FedSepsis: A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano Devices

Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application

National IoMT platform strategy portfolio decision model under the COVID-19 environment: based on the financial and non-financial value view

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