In most developing countries, it has become a severe challenge for the limited medical resources and outdated healthcare technology to meet the high demand of large population. From the perspective of social development, this unbalanced healthcare system in developing counties has also exacerbated the contradiction between physicians and patients, particularly those suffering from malignant diseases (such as prostate cancer). Rapid improvements in artificial intelligence, computing power, parallel operation, and data storage management have contributed significantly to a credible medical data decision-making on the detection, diagnosis, treatment, and prognosis of malignant diseases. Consequently, to address these existing problems in the current healthcare field of developing countries, this paper proposes a novel big medical data decision-making model exploiting fuzzy inference logic for prostate cancer in developing countries, constructing an intelligent medical system for disease detection, medical data analysis and fusion, treatment recommendations, and risk management. Based on 1 933 535 items of hospitalization information from over 8000 prostate cancer cases in China, the experimental results demonstrate that the intelligent medical system could be adopted to assist physicians and medical specialists in coming up with a more dependable diagnosis scheme.INDEX TERMS Prostate cancer, fuzzy inference logic, intelligent medical system, big medical data decision-making model, fusion of multimodal medical data, machine-assisted diagnosis.
In Wireless Body Area Networks (BAN), energy consumption, energy harvesting, and data communication are the three most important issues. In this paper, we develop an optimal allocation algorithm (OAA) for sensor devices, which are carried by or implanted in human body, harvest energy from their surroundings, and are powered by batteries. Based on the optimal allocation algorithm that uses a two-timescale Lyapunov optimization approach, we design a framework for joint optimization of network service cost and network utility to study energy, communication, and allocation management at the network edge. Then, we formulate the utility maximization problem of network service cost management based on the framework. Specifically, we use OAA, which does not require prior knowledge of energy harvesting to decompose the problem into three subproblems: battery management, data collection amount control and transmission energy consumption control. We solve these through OAA to achieve three main goals: (1) balancing the cost of energy consumption and the cost of data transmission on the premise of minimizing the service cost of the devices; (2) keeping the balance of energy consumption and energy collection under the condition of stable queue; and (3) maximizing network utility of the device. The simulation results show that the proposed algorithm can actually optimize the network performance.
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