Abstract
With the advanced telecommunication technologies, the patients can receive a good quality of medical care. In the past decades, many telecommunication technologies have been proposed and used to process patients’ electronic medical records (EMR). For the most of current EMR systems, patients only can get access to some simple medical files, for example, the personal details, description of health condition from physicians, chart records between patients and physicians, and payment bill, etc. However, with more detailed medical records, physicians could provide more effective medical services, which is not only saving the vital time for patients but also medical expenses. In this research, we proposed the systems that patients and physicians can get access to the EMR including medical images and videos with low transmission delay at any time. The primary objective of this research is to come up with new allocation systems and cache patient’s EMR in different location of the patient’s daily life. The patients and physicians can access EMR timely when needed. The proposed systems could decrease the transmission delay and provide more effective and efficient medical care for patients. Three systems have been proposed based on femtocaching, dynamic vision sensor, edge computing, game theory and deep Q-learning in this research.For the first system, we allocate patient’s medical records (text/words, images, and videos) to the femtocachings which are set up close to the nearest hospitals of the home area, workplace, family home, friends’ home and other places according to the patient’s social life. Femtocaching has been used to choose and cache the best medical files. Dynamic vision sensor has been proposed to decrease the size of medical video files due to the fact that the medical video files account for a large part of EMR. Dynamic vision sensor takes video according to the reflection of light and how fast the subjects move. It is the first time that femtocaching technology and dynamic vision sensor have been proposed in the health care area.
Different from the previous system, for the second system, an auction-based and non-cooperative game theory algorithm based on edge computing has been proposed for patients to share their edge devices to others instead of themselves only. Edge computing has been used to process medical records instead of cloud computing. There has been a large volume of research of cloud computing that has been conducted in the health care area in the last decade. However, there is an increase in the amount of medical data correlating with the rapidly increasing number of internet of things (IoT) devices. Cloud computing, especially, is not efficient enough for those health devices that require very short response times, this could cause intolerable network latency. Edge computing could cache and process computing tasks at the edge of the network without transferring to the cloud network and protect the privacy of patients. A competitive game mechanism is proposed by combining with the patient’s health condition and telecommunication channel quality on the edge. The system would compare the priority of patients and allocate medical files based on the results of the game theory algorithm.
In our third system, we proposed a new sharing algorithm based on deep Q-learning to allocate and cache the medical records of multiple patients. The new system provides more fair and humanized medical care services comparing with the last system. In our last system, all the medical parameters in the game theory allocation algorithm have the same weights, which would cause the situation that the patient has higher illness severity but lower priority. However, in this new system, the patients with a higher level of illness severity have higher priority and lower waiting time to be served. The system would not stop seeking the caching path for patients until the best Q value is found.
At last, according to the overall picture of the research conducted, the main conclusions together with some directions for the future works are presented.
| Date of Award | 1 Nov 2019 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Mohammed Shikh-Bahaei (Supervisor) & Paul Luff (Supervisor) |