Build an application to monitor SpO2, heart rate, body temperature and use AI to analyze and warn timely clinical symptoms

Abstract

Telemedicine, or remote healthcare, is a medical service that enables physicians and patients to interact and exchange information without being physically present in the same location. It utilizes digital devices such as computers and smartphones to deliver healthcare services. Typically, telemedicine involves live video consultations, although some providers may also offer services through email or text messages. This approach encompasses various aspects of medical care, including telemedicine consultations, prescriptions, remote monitoring of health indicators, and remote diagnosis and treatment. Many individuals opt to utilize telemedicine services through their regular healthcare providers, while others access care through dedicated apps or platforms. Remote consultations involve the use of technology to exchange information, receive diagnosis, and obtain advice from healthcare professionals, even when the patient and doctor are physically separated. Telemedicine offers several advantages. It allows patients to save time by avoiding the need for travel and eliminates the hassles associated with traffic and scheduling conflicts. Additionally, telemedicine reduces the risk of exposure to infectious diseases that may be present in healthcare settings. However, it is important to note that not all medical cases are suitable for telehealth consultations. This approach is particularly beneficial for patients who are located far away from healthcare facilities, elderly or physically weak individuals who have difficulty walking, individuals who require post-surgical follow-ups, or those with chronic diseases. In this thesis, The application carried out several key tasks related to telemedicine and utilizing artificial intelligence models for data collection and disease diagnosis. The application collected data from patients through remote healthcare IoT devices such as SpO2 (blood oxygen level) measurements, heart rate, and temperature.For each measure we used compatible devices and technologies to gather these indicators from patients during the telemedicine process. After collecting the data, the app processed and analyzed the data to extract useful and relevant information, as well as display it on observational charts. Before integrating an AI algorithm into my application, I conducted thorough testing and evaluation of various artificial intelligence models for disease diagnosis using datasets collected from telemedicine in previous projects. These models were developed using machine learning or deep learning algorithms. The primary goal of this evaluation was to ensure the accuracy and reliability of the models in remote diagnosis. 7 During the evaluation process, I considered several metrics to assess the usability and performance of the models. These metrics included accuracy, sensitivity, specificity, and other relevant measures. By analyzing these metrics, I could determine the effectiveness of the models in correctly diagnosing diseases based on the collected data. The training and evaluation phase played a crucial role in fine-tuning the AI models and optimizing their performance. It allowed me to identify any potential limitations or areas for improvement. This rigorous evaluation process ensured that the selected AI model used in my application was well-suited for accurate and reliable remote disease diagnosis. In summary, my application aims to harness the advantages of telemedicine, including convenience and efficiency in delivering healthcare services remotely. It offers numerous benefits, such as time savings, reduced exposure to infectious diseases, and improved accessibility for specific patient groups. However, it is crucial to consider the suitability of telehealth consultations based on individual circumstances and specific medical conditions. By leveraging the capabilities of artificial intelligence and telemedicine, we can enhance healthcare delivery and ultimately improve patient outcomes. The evaluation of AI models using telemedicine datasets has provided valuable insights and instilled confidence in the performance and usability of the chosen model for my application