Intelligent Internet of Things Medical Technology in Implantable Intravenous Infusion Port in Children with Malignant Tumors

Abstract

Due to the recent technological revolution that is centered around information technology, the Internet of Medical Things (IoMT) has become an important research domain. IoMT is a combination of Internet of Things (IoT), big data, cloud computing, ubiquitous network, and three-dimensional holographic technology, which is used to build a smart medical diagnosis and treatment system. Additionally, this system should automate various activities, such as the patient's health record and health monitoring, which is an important issue in the development of modern and smart healthcare system. In this paper, we have thoroughly examined the role of a smart healthcare system architecture and other key supporting technologies in improving the health status of both indoor and outdoor patients. The proposed system has the capacity to investigate and predict (if feasible) the clinical application and nursing effects of totally implantable intravenous port (TIVAP) in pediatric hematological tumors. For this purpose, seventy children with hematologic tumors were treated with TIVAP, and IoMT-enabled care was provided to them, where the occurrence of adverse events, specifically after the treatment, was observed. The experimental results collected after the 70 children were treated and cared for by TIVAP show that there were five cases of adverse events, whereas the incidence rate of the adverse events was 7.14%. Moreover, TIVAP has significant efficacy in the treatment of hematologic tumors in children, and it equally reduces the vascular injury caused by chemotherapy in younger patients. Likewise, targeted care reduces the incidence of adverse events in children with expected ratio.

Figure 1

The schematic diagram of an infusion port.

Figure 2

RFID-roll call architecture.

Figure 3

The three functions of the proposed roll-call system's process.

Figure 4

Child Results (the best clustering obtained using the Lupin metric is at K = 3, which incidentally also corresponds to the lowest misclassification error): (a) the confusion matrix of level 1 nodes, (b) error in the classification of different clusters, and (c) the mirror matrix of different clusters at k = 3.

Figure 5

Children-doctor Results (even though the misclassification error is the lowest at K = 3, the best clusters obtained using the Lupin metric are at K = 6. As we expect, i.e., the Lupin metric encourages partitioning into more clusters while keeping the error low): (a) the confusion matrix of level 1 nodes, (b) error in the classification of different clusters, and (c) the mirror matrix of different clusters at k = 3.

Figure 6

The schematic diagram of body placement in infusion port.

Figure 7

The body placement of children in infusion port.