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Background
Despite apparent promise and the availability of numerous examples in the literature, machine learning models are rarely used in practice in ICU units. This mismatch suggests that there are poorly understood barriers preventing uptake, which we aim to identify.
Methods
We begin with a qualitative study with 29 interviews of 40 Intensive...
Citations
... Banerjee et al. 7 enhanced the user experience and service resiliency in the event of an emergency; FC techniques have been utilized to link IoT with real-time computation at edge networks. Fog edge computing, with its dispersed design and proximity to end-users, may deliver quicker reaction times and higher quality services for IoT use. ...
... Banerjee et al. 7 Enhanced user experience and service resiliency in the event of an emergency. ...
Fog computing is an emerging technology that extends the capability and efficiency of cloud computing networks by acting as a bridge among the cloud and the device. Fog devices can process an enormous volume of information locally, are transportable, and can be deployed on a variety of systems. Because of its real-time processing and event reactions, it is ideal for healthcare. With such a wide range of characteristics, new security and privacy concerns arise. Due to the safe transmission, arrival, and access, as well as the availability of medical devices, security creates new issues in the area of healthcare. As an outcome, fog computing necessitates a unique approach to security and privacy metrics, as opposed to standard cloud computing methods. Hence, this paper suggests an effective blockchain depending on secure healthcare services in fog computing. Here the fog nodes gather the information from the medical sensor devices and the data is validated using smart contracts in the blockchain network. We propose a Functional Biased Elliptic Curve Cryptography Algorithm (FB-ECC) to encrypt the data. The optimization is performed using Galactic Bee Colony Optimization Algorithm (GBCOA) to enhance the procedure of encryption. The performance of the suggested methodology is assessed and contrasted with the traditional techniques. It is proved that the combination of fog computing with blockchain has increased the security of data transmission in healthcare services
... Constrained Application Protocol (CoAP) [25] and Datagram Transport Layer Security (DTLS) [26] protocol was used there to protect the precious sensor resources. In the study of paper [27], the authors proposed an IoT and machine learning-based framework to provide a better and smarter healthcare experience, especially while real-time monitoring critical patient's conditions in ICU. They used blockchain technology to ensure the security of the framework. ...
... The Constrained Application Protocol (CoAP) [25] and Datagram Transport Layer Security (DTLS) [26] protocol were employed to protect the valuable sensor resources. Additionally, in the study detailed in paper [27], the authors proposed an IoT and machine learning-based framework to enhance healthcare, particularly in real-time monitoring 531 of critical patient conditions in the ICU. They utilized blockchain technology to ensure the security of the framework and leveraged fog computing to reduce communication latency and enhance system robustness. ...
In the realm of critical care, the need for precise and continuous monitoring of patients is paramount. This paper explores the development of an advanced system that leverages the capabilities of the Internet of Things (IoT) to facilitate the meticulous tracking of a patients vital signs in Intensive Care Units (ICUs). The system is engineered to minimize the potential for human error by providing uninterrupted monitoring of various health indicators such as body temperature, blood oxygen saturation (SpO2), heart rate, blood pressure, and electrocardiogram (ECG) readings. Additionally, it can analyze patient fluids for glucose, lactate, blood circulation, red and white blood cell counts, as well as calcium and potassium levels. The system employs fog nodes as a means to store and process the data collected from patients. These nodes generate comprehensive health reports based on the collected data and store them in a cloud-based platform. This setup allows healthcare professionals, including doctors and ICU staff, to access real-time patient data and reports from any location at any time. The system is designed to alert the medical team in case of any detected anomalies in the patients health parameters. This paper also provides a comparative analysis of previous research conducted on smart IoT devices used in ICU patient monitoring systems, highlighting the unique advantages offered by the proposed system. The paper concludes with a discussion on potential future enhancements, such as bolstering data security measures and incorporating machine learning techniques for improved system performance.
... Labeled data, or what is known as training set [53], is crucial in supervised learning because it provides the algorithm with the ground truth information that is needed to learn and make predictions. Labeled data consists of input features (independent variables) [54,55] and their corresponding correct target values (dependent variable) or labels [56]. The algorithm uses these labeled examples to identify patterns, associations, and relationships within the data, allowing it to learn how to make predictions on new, unlabeled data. ...
Cardiovascular disease is the leading cause of global mortality and responsible for millions of deaths annually. The mortality rate and overall consequences of cardiac disease can be reduced with early disease detection. However, conventional diagnostic methods encounter various challenges, including delayed treatment and misdiagnoses, which can impede the course of treatment and raise healthcare costs. The application of artificial intelligence (AI) techniques, especially machine learning (ML) algorithms, offers a promising pathway to address these challenges. This paper emphasizes the central role of machine learning in cardiac health and focuses on precise cardiovascular disease prediction. In particular, this paper is driven by the urgent need to fully utilize the potential of machine learning to enhance cardiovascular disease prediction. In light of the continued progress in machine learning and the growing public health implications of cardiovascular disease, this paper aims to offer a comprehensive analysis of the topic. This review paper encompasses a wide range of topics, including the types of cardiovascular disease, the significance of machine learning, feature selection, the evaluation of machine learning models, data collection & preprocessing, evaluation metrics for cardiovascular disease prediction, and the recent trends & suggestion for future works. In addition, this paper offers a holistic view of machine learning’s role in cardiovascular disease prediction and public health. We believe that our comprehensive review will contribute significantly to the existing body of knowledge in this essential area.
... Banerjee [30] proposed a model that uses machine learning to make decisions in the intensive care unit (ICU) under the fog environment of the Internet of Things. The proposed model performed real-time processing by bringing the computation closer to the data source. ...
Urine infections are one of the most prevalent concerns for the healthcare industry that may impair the functioning of the kidney and other renal organs. As a result, early diagnosis and treatment of such infections are essential to avert any future complications. Conspicuously, in the current work, an intelligent system for the early prediction of urine infections has been presented. The proposed framework uses IoT-based sensors for data collection, followed by data encoding and infectious risk factor computation using the XGBoost algorithm over the fog computing platform. Finally, the analysis results along with the health-related information of users are stored in the cloud repository for future analysis. For performance validation, extensive experiments have been carried out, and results are calculated based on real-time patient data. The statistical findings of accuracy (91.45%), specificity (95.96%), sensitivity (84.79%), precision (95.49%), and f-score(90.12%) reveal the significantly improved performance of the proposed strategy over other baseline techniques.
... WBANs are smarter, have a smaller size, have a shorter battery life, have higher quality of service (QoS) requirements, and handle diverse organisation traffic [2]. The Web of Things (IoT) is a unique innovation that connects any item to a company, and this approach is ideal for WBAN engineering of medical care administrations [3], [4]. The constant advancement of innovation that prepares for the expansion of relationships over the internet and the development of the capacity to deal with data has created more significant chances for the global health business, specifically telemedicine. ...
... The current examination expects to present a novel engineering (SENET) [3], which depends on AI methods and comprises of three principle layers. It is pointed toward bringing the calculations near information sources from medical care habitats [4]. A structure introduced [5] that utilizes, fog computing alongside IoT and AI to give a superior and more intelligent medical services insight. ...
... The authors used the k-nearest neighbor approach to classify and validate the model. In 2020, Banerjee [25] proposed a model that uses machine learning to make decisions in the intensive care unit (ICU) under the fog environment of the Internet of Things. The proposed model performed real-time processing by bringing the computation closer to the data source. ...
Urine infections are one of the most prevalent concerns for the healthcare industry that may impair the functioning of the kidney and other renal organs. As a result, early diagnosis and treatment of such infections are essential to avert any future complications. Conspicuously, in the current work, an intelligent system for the early prediction of urine infections has been presented. The proposed framework uses IoTbased sensors for data collection, followed by data encoding and Infectious Risk Factor computation using the XGBoost algorithm over the fog computing platform. Finally, the analysis results along with the healthrelated information of users are stored in the cloud repository for future analysis. For performance validation, extensive experiments have been carried out and results are calculated based on real-time patients’ data. The statistical results of accuracy(91.45%), specificity(95.96%), sensitivity(84.79%), precision(95.49%), and f-score(90.12%) reveal the significantly improved performance of the proposed strategy over other baseline techniques.
... Constrained Application Protocol (CoAP) [25] and Datagram Transport Layer Security (DTLS) [26] protocol was used there to protect the precious sensor resources. In the study of paper [27], the authors proposed an IoT and machine learning-based framework to provide a better and smarter healthcare experience, especially while real-time monitoring critical patient's conditions in ICU. They used blockchain technology to ensure the security of the framework. ...
Internet of Things (IoT) has enormous capability to revolutionize medical care and enhance global health indicators in the health sector. The Intensive Care Unit (ICU) is an important part of the medical sector because it treats patients who have suffered a terrible accident, have a very serious or life-threatening condition or are undergoing an effective medical treatment that necessitates 24-hour care and support. For this reason, one of the most important issues that hospitals suffer is lack of patient monitoring systems cause ICU health attendants and doctors were overburdened, which can lead to medical errors due to a huge workload that exceeded their ability. As a result, even a minor delay in recognizing a patient’s deterioration might result in severe disability or death. Therefore, continuously patient monitoring in ICU is the most crucial process. That’s why a way to optimize the ICU monitoring procedure in order to minimize delayed detection and reduce the workload of ICU doctors and caregivers is needed. In this study, we proposed a smart IoT-based ICU patient monitoring system to help doctors and hospitals to monitor the patients continuously and in making quick decisions. Our proposed method can measure patient’s body parameters (temperature, SpO2, heartbeat, blood pressure, ECG, and it can also measure glucose, lactate, blood circulation, red blood cells, white blood cells, calcium, potassium from the patient fluids) in real-time and in case of anomalous values of the patient’s body parameters, the device will send a notification to the assigned doctor and the Emergency Care Unit (ECU) of the hospital. Doctors can also monitor the patient remotely through our system.
... where machine learning can be a solution, . Also, the IoT devices can be used to gather physiological signals of the patients for further processing, which is supported by machine learning techniques, Banerjee et al. (2020). The machine learning techniques can be solved by using Nature Inspired algorithms as discussed by Kauser et al. (2017). ...
Intelligent predictive systems are showing a greater level of accuracy and effectiveness in early detection of critical diseases like cancer and liver and lung disease.Predictive models assist medical practitioners in identifying the diseases based on symptoms and health indicators like hormone,enzymes,age,bloodcounts,etc.This study proposes a framework to use classification models to accurately detect chronic liver disease by enhancing the prediction accuracy through cutting-edge analytics techniques.The article proposes an enhanced framework on the original study by Ramana et al. (2011).It uses evaluation measures like Precision and Balanced Accuracy to choose the most efficient classification algorithm in INDIA and USA patient datasets using various factors like enzymes,age,etc.Using Youden’s Index, individual thresholds for each model were identified to increase the power of sensitivity and specificity.A framework is proposed for highly accurate automated disease detection in the medical industry,and it helps in strategizing preventive measures for patients with liver diseases.
... The management platform comprises development, examination, and pathological testing equipment distributed through each medical section workplace, a testing data acquisition management system, a testing database AI cloud and a testing equipment management system. Most current studies have focused on using IoT technology either in patients' health tracking [29], [18], [30], [35], [36], [29], [42], [44], [37], [45], [46], [47], [48], decision support systems [32], [49] or survey works that present its role in pandemic situations [34], [31]. Some works have been done in Medical Equipment Management field using IoT technology [39], [41], [40]. ...
... It is suggested to use blockchain technology to validate data integrity between the different data stations on MEMS. Some works have suggested a similar solution as in Anwesha Banerjee et al. [49]. ICU webserver can be vulnerable to attacks such as Cross-Site Scripting (XSS), phishing, SQL Injection (SQLi), Path Traversal, Distributed Denial of Service (DDoS), and data leakage. ...
Recently, COVID-19 has infected a lot of people around the world. The healthcare systems are overwhelmed because of this virus. The intensive care unit (ICU) as a part of the healthcare sector has faced several challenges due to the poor information quality provided by current ICUs’ medical equipment management. IoT has raised the ability for vital data transfer in the healthcare sector of the new century. However, most of the existing paradigms have adopted IoT technology to track patients’ health statuses. Therefore, there is a lack of understanding on how to utilize such technology for ICUs’ medical equipment management. This paper proposes a novel IoT-based paradigm called IoT Based Paradigm for Medical Equipment Management Systems (IoT MEMS) to manage medical equipment of ICUs efficiently. It employs IoT technology to enhance the information flow between medical equipment management systems (THIS) and ICUs during the COVID-19 outbreak to ensure the highest level of transparency and fairness in reallocating medical equipment. We described in detail the theoretical and practical aspects of IoT MEMS. Adopting IoT MEMS will enhance hospital capacity and capability in mitigating COVID-19 efficiently. It will also positively influence the information quality of (THIS) and strengthen trust and transparency among the stakeholders.
... where machine learning can be a solution, . Also, the IoT devices can be used to gather physiological signals of the patients for further processing, which is supported by machine learning techniques, Banerjee et al. (2020). The machine learning techniques can be solved by using Nature Inspired algorithms as discussed by Kauser et al. (2017). ...
In recent years, intelligent systems are proven to be more accurate and effective in medical industry in terms of detection of diseases based on different health indicators. Predictive modeling has been one of the broadly used intelligent techniques for automated detection of multiple diseases like cancer, cardiac arrhythmia, liver disease, lungs infection etc. These systems are assisting medical practitioners in early detection and setting preventive measures. This research focuses on making classification models more accurate for one of the commonly found health problem in countries like India and USA: Chronic Liver Disease. Our approach is based on enhancing the classification models prediction accuracy using cutting-edge analytics on the original study by Ramana et al. (2011).The research focuses on two patient data sets (India and USA) and uses the measures like Precision and F1 – Score to propose efficient classification algorithm for liver disease detection from various levels of enzymes, age etc. The use of Youden’s Index defines separate threshold for each of the model to enhance the power of Sensitivity and Specificity of each model respectively. This research has serious implications on rapidly evolving medical industry in terms of automated disease detection, early detection and strategizing preventive measures for patients.
