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All over the world, there is a lot of patient health data in different locations such as hospitals, clinics, insurance companies, and other organizations. In this sense, global identification of the patient has emerged as an everyday healthcare challenge. Governments and institutions have to prioritize satisfactory, quick, and integrated decision-m...
Citations
... They found that most studies employed convolutional neural networks and support vector machine techniques, which exhibited outstanding performance in disease prediction. Addressing a large number of patients' health data, Costa et al. [6] introduced a decentralized software model based on blockchain and intelligent control for identity recognition. In the field of medical Internet of Things (IoT), Chen et al. [7,8] established an IoT-based regional collaborative emergency response system for patients with ST-segment elevation myocardial infarction, resulting in significant reductions in patients' treatment time and mortality rates. ...
The data input process for most chest pain centers is not intelligent, requiring a lot of staff to manually input patient information. This leads to problems such as long processing times, high potential for errors, an inability to access patient data in a timely manner and an increasing workload. To address the challenge, an Internet of Things (IoT)-driven chest pain center is designed, which crosses the sensing layer, network layer and application layer. The system enables the construction of intelligent chest pain management through a pre-hospital app, Ultra-Wideband (UWB) positioning, and in-hospital treatment. The pre-hospital app is provided to emergency medical services (EMS) centers, which allows them to record patient information in advance and keep it synchronized with the hospital's database, reducing the time needed for treatment. UWB positioning obtains the patient's hospital information through the zero-dimensional base station and the corresponding calculation engine, and in-hospital treatment involves automatic acquisition of patient information through web and mobile applications. The system also introduces the Bidirectional Long Short-Term Memory (BiLSTM)-Conditional Random Field (CRF)-based algorithm to train electronic medical record information for chest pain patients, extracting the patient's chest pain clinical symptoms. The resulting data are saved in the chest pain patient database and uploaded to the national chest pain center. The system has been used in Liaoning Provincial People's Hospital, and its subsequent assistance to doctors and nurses in collaborative treatment, data feedback and analysis is of great significance.
In recent years, healthcare crisis have demonstrated how overwhelmed hospitals can bring the world to a standstill. The objective of this project is to provide quality medical care remotely through the use of a medical tablet that connects to easy-to-use affordable sensors, allowing patients to have precise consultations from the comfort of their homes. This would enable hospitals to focus on priority cases, reducing waiting lists and freeing up resources for face-to-face consultations. Additionally, the project would be especially beneficial for smaller towns without nearby health centers, where elderly individuals with fragile health conditions are often forced to travel long distances for medical care.KeywordsRemote SensingSmart HealthcareTelemedicineMonitoring PlatformHomecare
