Introduction
Diabetes is a chronic health condition, there are two forms of diabetes Type 1 which affects the body's ability to produce insulin which is easily rectified by insulin shots, type 2 diabetes is when the body does not react to insulin which can be fatal thus, the challenge that i will be tackling concerning diabetic care is poor medical adherence which is noted to be significantly more common in type 2 diabetes. Poor medical adherence is important in type 2 diabetes for many reasons, for example the long term effects of not taking diabetic medication is risking blindness and also kidney damage. In addition to this further down the road it can lead to heart failure. In this report I will be showing/explain how the use of Ai could assist with the challenge of diabetic medical adherence an example could be to set a reminder or what dosage an individual should take.
Background
AI has been said to be a subdivision of computer science whose main goal is to create a program/method that assists via analysing data and simplifying the use of it in a wide range of areas of internet technologies. The way that ai applies to diabetic care is simple yet enticing for easy and efficient data handling and the various tools and devices used for its management. To ensure safer use of technology through AI, it is advised to possess designs which ensure safety and security, backups and procedures in place in order to keep everything safeguarded with all uncertainties noted and looked up for the various systems.
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Due to recent technological advancements, various forms of technology allow for the monitoring and and tracking of patients symptoms and disease status. Examples include smartphones and wearables. In order to effectively treat diabetes health care professionals and physicians must give patients the option of choosing AI assisted care.
There are 3 key areas of diabetic care. These include patients with diabetes, health care professionals, and health care systems. Patients with diabetes now have newer dimensions of self care. They also have decision making that is fast and reliable. Diabetes patients also have variable follow ups for health care providers as well as an optimised utility of resources within healthcare systems.
There are four areas in which diabetic care could be improved through the use of better AI. Firstly, automated retinal screening the ai system currently used in this area is used to detect diabetic retinopathy, maculopathy and any other differences compared to normal findings. The second area is clinical decision support which is a system that provides health professionals a method to assist with clinical decision tasks. AI application to this is used to detect and monitor and monitor diabetes and any other diseases. The third is predictive population risk stratification, this is used to predict the future of an individual in this case the future of a diabetic patient which could help plan different possible scenarios making it easier to deal with problems that may occur. The most common clinical AI application is that it identifies those with diabetes who are at a higher risk concerning different complications that may or may not be faced as well as risk of hospitalization. Last but not least the fourth is patient self management which is a system in place that assists patience via increasing their skills and confidence in managing their own health issues. One of many AI applications that are used is improved glucose sensors which are improved through the use of AI as well as other methods such as activity and dietary tracking devices.
Methodology and Data
There are numerous strategies of dealing with the treatment of diabetic patients. One example would be Case-based reasoning. This is a form of AI used to fix issues where the solution has already been accuired from past experiences. CBR has been used for the diabetes support system. The purpose of this system is to find issues with the regulation of blood glucose and to offer solutions to these issues. This system also allows for the effective and ineffective treatment of a patient. CBR can provide insulin therapy for meal situations in diabetes.
The targeted methods of solving medical adherence that we will be focusing on is CBR also known as case based recording and machine learning. CBR as stated in the first paragraph is a form of ai that we utilise in order to correct issues that we have already faced in the past such as any type of glitches they had or updating information as we discover new and better ways of dealing with medical adherence in diabetes. This links to the challenge that we are facing due to the fact that it automatically detects the glucose levels in blood which could send out an alert to the individual who has the medical adherence issue reminding them. Another method that could be put in place is an ai based alert which alerts the diabetic individual that they should take their medicine. In addition to this the AI could help answer basic questions the individual has in mind and could possibly provide links to various websites for which they can do their own research to help them manage their condition.
Knowledge representation is another area of AI where the AI is dedicated to solving important tasks such as diagnosing medical conditions by utilising the computer system it possesses. The way knowledge representation links to medical adherence is that every computer system is assisting people with diabetes so there are less people every year that don't take their medication. The datasets that can be used are various websites through the internet. This also could be useful to us as it conveys bits of info to the individual so that they learn more about their condition and how they can manage it themselves. The datasets could be stored on different programmes such as a spreadsheet database in figures. In addition to this there are many types of datasets the ai could utilise for example numerical datasets.
The A.I system discussed in this report provides accurate information and reasons as to why they should be taking their medication as well as any issues that may arise as a result of not taking them. An example could be the AI could scan websites and display different numerical values/data to the patient and slowly but surely help them understand their medical condition and teach them valuable skills in their medical issue to help them with medical adherence. In addition to this the AI could have basic interaction in the form of questions and answers to help with any queries the patient may have.
Analysis and Discussions
The AI we made worked as expected for example it sent out an alert at a certain time reminding the individual to take their medication and linking a random website which the individual could potentially use for themselves and learn all about their condition so they have a better understanding on how to manage their condition and reasons to take their medication. Also it linked a website to which they can use in order to adjust their diet so they can make the best of what they have. In addition to this the ai is able to answer a few questions such as “Could you send me some information on diabetes?” and “what can you tell me about diabete?”. Ways that we can improve on it is we can make AI more interactive and provide more than just a link. In addition to this we could've made it so the AI checked up on the individual throughout the day and placed all the data they gather from the individual into a spreadsheet/database so that it could be viewed whenever they like.
Conclusions
This coursework has taught me the importance of artificial intelligence and how it impacts diabetic care. The A.I system discussed in this report allows sufferers of diabetes to receive reliable and accurate information about their medication as well as the adverse effects of not taking them. However, this system also has its limitations. For example this system doesn't
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