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EDUC5405 Advanced Educational Research

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EDUC5405 Advanced Educational Research

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EDUC5405 Advanced Educational Research

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Course Code: EDUC5405
University: University Of Glasgow is not sponsored or endorsed by this college or university

Country: United Kingdom

Develop deep understanding of research ethics in high education study . Collect and analyse ethics related information to deal with machine learning in medicine research work. Have a comprehensive understanding of ethics policy used in your education institute, state and residential nation. write a report on the ethics issues that may happen. You need to consider both pros and cons of certain type of research. Together with the ethics issues you will consider in the chosen case, you also need to provide the response plan to solve the problems according to the ethics policies, principles, rules in VU, VIC state and Australia.
For each:
aspect, you also need to provide response actions, solutions for the ethical issues, integrity and safety issues and risks that may occur.

Introduction to IT Educational Research
In this paper we would discuss regarding ethics in high education study. We have chosen a particular topic under that. The topic is ethics related information for dealing with machine learning in medicine (Gui and Chan 2017). The report mentions the background of the research. It discusses regarding the pros and cons in the field of machine learning in medicine. Various issues and challenges faced by the research are also mentioned in the report. A response plan to the issues and risks has been provided.
Research background
Machine learning in medicine
Research in machine learning for various methods of medical applications stay centered on numerous technological issues. They are mostly driven by application (Cabitza, Rasoini and Gensini 2017). Machine learning has been used for carrying out the analysis regarding the importance of various clinical parameters as well as the combinations for prognosis. Machine learning is used in medical field for predicting disease progression, extracting knowledge of medicine, therapy planning as well as support, patient management and many more (Leung, Delong and Alipanahi 2016). Machine learning in medical science has been used for data analysis like detecting regularities in the data by dealing appropriately with data that are imperfect, interpretation of the continuous data that is used by Intensive Care Unit.
Ethics in using machine learning in medicine
Similar to other fields, ethics has to be followed in the medical field as well. The policies of ethics are followed by the members of the field. The ethics regarding the functioning of machine learning is a very common thought that arises when the significant advancements of these fields are analyzed (Chen and Asch 2017). The main ethical issue is that weather the usage of machine learning would replace the labor. Machine learning has the ability to aid various medical practices. Besides numerous advantages provided by machine learning, it also provides many disadvantage that can be harmful for the medicine field. It is very interesting to use machine learning in the field of medicine. The techniques of machine learning can learn from huge amount of information regarding healthcare that is presently available. This data can be used for assisting various clinical decisions making. Researchers have found out some consequences that can be met if machine learning is used at an increased level.
Pros and Cons of research
Some advantages of using machine learning in medicine are as follows

Machine learning can be used in order to handle multi-variety as well as multi-dimensional data in the dynamic environments. Medicine field consists of numerous data regarding the patients (Koprowski and Foster 2018). These data should be kept in such a way that they are accessed whenever necessary.
Machine learning provides real-time predictions and fast processing. It helps the professionals to predict diseases within the patients (Lasko, Walsh and Malin 2017). It also helps to carry out the entire process by consuming less time.
Machine learning in medicine provides continuous quality with huge as well as complex environment of processes. Machine learning makes the working environment a bit complex but it delivers quality outcome.

Machine learning has numerous disadvantages as well. They are mentioned below

Data: This is one of the biggest disadvantages of using machine learning in medicine. A good machine learning system requires a lot of data. The main issue here is that where the professional get data and what would is to be done if the desired data does not actually exist. In some cases, data sought already exists. This data sought might be free and open sourced (Goodfellow, Bengio and Courville 2016). It might be available to public in return of some amount. This data might also be owned by a specific group of people. In the field of medicine, this is very risky because if the data related to patients or professionals in revealed, it would be harmful for the institute as well as the data owners. Someone might use this available data for ill purposes.
Algorithm: In most of the case, the institutes are busy securing their data and forget to secure their algorithms. Usually everyone has access to the algorithms used by various systems. People are also allowed to modify them according to their needs (Motwani, Berman and Germano et al., 2016). In the field of medicine, algorithms might be changed based on the necessities but allowing the access of the algorithms to everyone might be harmful.

Ethics issues, integrity and safety issues and risks
Ethical issues

Unemployment: This is one of the major ethical issues faced by health institutes that use machine learning in various processes (Yoo, Ramirez and Liuzzi 2014). Machine learning has replaced the human labor to some extent which brings about unemployment among young people who are aspiring for jobs.
Stupidity: Machines can be very precise and more accurate than humans but the fact has to be considered that machines do not have brains or feelings (McDonald, Ramagopalan and Cox 2017). In sensitive cases like handling serious patients human labor should b preferred because machines would not be able to differentiate between sensitive cases and normal cases.

Safety and integrity issues
Safety issues are one of the major issues faced by the medical institutes. The data owned by them are to be saved in such a way that they are accessed by authorized users only. The field of medicine owns data regarding various patients and their medications (Yoo, Ramirez and Liuzzi 2014). These data are very sensitive and are to be saved in proper manner. The organizations use data produced by machines in order to predict better results for future.
Using machine learning in medicine makes the field face many risks. One of the risks includes accuracy. Medicine is a field which requires accurate data. Improper data might impose serious issues to the patients as well as institute. There are various potential risks that can be faced by the organization due to inaccurate data (Yoo, Ramirez and Liuzzi 2014). Consistent accuracy of data maintains the people’s trust in technology. One more risk includes security of data. Security of data is the major concern of any medical institute. Exposure of data regarding patients might result in disclosing their personal data such as name, address, health information, financial information and many more.
Response plan to the issues and risks
                                                                                       Figure 1: Response plan for the issues and risks
From the above report it can be concluded that machine learning has been at the edge of technology. Every business is using machine learning for automating their process, reduce human labor and increase the perfection of operations carried out. Machine learning plays a vital role in every business including medicine. Various applications of machine learning that are used by medicine are disease identification, personalized treatment, drug discovery, clinical trial research, radiology as well as radiotherapy, electronic health records and prediction of epidemic outbreak. In the field of medicine, besides various advantages it also has disadvantages that can be very harmful for the field.
Cabitza, F., Rasoini, R. and Gensini, G.F., 2017. Unintended consequences of machine learning in medicine. Jama, 318(6), pp.517-518.
Chen, J.H. and Asch, S.M., 2017. Machine learning and prediction in medicine—beyond the peak of inflated expectations. The New England journal of medicine, 376(26), p.2507.
Goodfellow, I., Bengio, Y., Courville, A. and Bengio, Y., 2016. Deep learning (Vol. 1). Cambridge: MIT press.
Gui, C. and Chan, V., 2017. Machine learning in medicine. University of Western Ontario Medical Journal, 86(2), pp.76-78.
Koprowski, R. and Foster, K.R., 2018. Machine learning and medicine: book review and commentary.
Lasko, T.A., Walsh, C.G. and Malin, B., 2017. Benefits and Risks of Machine Learning Decision Support Systems. Jama, 318(23), pp.2355-2355.
Leung, M.K., Delong, A., Alipanahi, B. and Frey, B.J., 2016. Machine learning in genomic medicine: a review of computational problems and data sets. Proceedings of the IEEE, 104(1), pp.176-197.
McDonald, L., Ramagopalan, S.V., Cox, A.P. and Oguz, M., 2017. Unintended consequences of machine learning in medicine?. F1000Research, 6.
Motwani, M., Dey, D., Berman, D.S., Germano, G., Achenbach, S., Al-Mallah, M.H., Andreini, D., Budoff, M.J., Cademartiri, F., Callister, T.Q. and Chang, H.J., 2016. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. European heart journal, 38(7), pp.500-507.
Yoo, C., Ramirez, L. and Liuzzi, J., 2014. Big data analysis using modern statistical and machine learning methods in medicine. International neurourology journal, 18(2), p.50.

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