Novel AI-based Prediction Approach for Improving Patient Treatment in Healthcare

• Published in: 2023 2nd International Conference on Ambient Intelligence in Health Care (ICAIHC) pp. 1 - 5
• Main Authors: Thatikonda, Ramya, Vaddadi, Srinivas Aditya, Dash, Bibhu, Panigrahi, Amrutanshu, Pati, Abhilash, Sahu, Bibhuprasad
• Format: Conference Proceeding
• Language: English
• Published: IEEE 17-11-2023
• Subjects: Artificial intelligence; Artificial Intelligence (AI); Auto-encoder; Cancer; Cancer Disease; Costs; Data extraction; e-Health; Patient monitoring; Performance analysis; Real-time systems; Scalability
• DOI: 10.1109/ICAIHC59020.2023.10431447

The healthcare industry and e-health have benefited greatly from the proliferation of automated methods available in today's technological landscape. While many issues need to be addressed during cancer treatment, cardiac issues are at the forefront and require constant therapy and monitoring. Data extraction and prediction for cancer patients are complicated by heterogeneity. This research also addresses the problem of improper data accessibility, which is especially important to resolve for healthcare-related patient data. To address this issue, we present a Health System powered by AI and the Internet of Things that can improve the treatment process by boosting patient monitoring through metrics like cost and length of stay. By deploying the automated AI-based health system to integrate AI and IoT for the real-time health monitoring system, we discuss how effective prediction aids the doctor in processing in-progress treatment based on the pretreatment information. The pre-treatment and post-treatment phases have been combined here. With the use of performance analysis, we can successfully retrieve the patient's preference history while keeping an eye on their health in terms of both cost and resource consumption. Scalability and dependability for cutting-edge e-health advancements form the basis of this evaluation of the framework's validity.