Use of Machine Learning in Healthcare

Research Objectives

This task is based on project report have to make a poster for that the topic is the use of machine learning in healthcare. This is reassessment task the previous work is done by you guys both the poster and report unfortunately i got failed in poster and i got just passing marks in report. Now for the reassessment have to make nice poster again. I am adding to files one is report file and another you can see the poster and feedback comments given by supervisor

Moving from paper charts to electronic health records (EHR) have gained access to volumes of data of the patients. As per the history, organizations are able to mine all of those data that are structured and these types of data account for only 20 percent of healthcare data (Chui, Manyika and Miremadi 2018). The unstructured data make up the other 80 percent and contains sensitive details about the health of patients, conditions, treatments and can be said to be challenging for the computer system to analyse. Thus, these in general go unused by payers or by the providers. All of this condition is encountering changes with the advancements happening in fields such as Artificial Intelligence (AI).

Machine learning (Ml) refers to the type of AI that permits software applications to be more accurate at predicting the outcomes without being extensively programmed to do so (Tarafdar, Beath and Ross 2019). ML algorithms make use of historical data in the form of input and predict the new output values. ML provides the computers with the capability to learn without being run on any specific program. It is considered as one of the exciting technologies that the different sectors are adopting at a good pace. It has the potential to power different services that are generally in use these days.

Healthcare is a sector that is never out of demand and mainly in time of crisis such as coronavirus, the sector experiences a lot of pressure. Without embracing new technologies it is not possible to handle such pressures. Machine learning helps the sectors to go for automation and can be said to be prerequisite to become fully digital. The various algorithms in ML figure out the different patterns in data that generate insights and help in making better decisions along with predictions (Daugherty and Wilson 2018). Then there is choosing proper algorithm and checking on for which specific application, ML needs to be integrated with the operations. Apart from that there is a need to have proper knowledge about the ethical issues associated with the technology so that these can be considered while adopting the technology. The paper will investigate the way in which machine learning is used in healthcare sector.

Significance of Research

The main aim of the research is to figure out how machine learning can be made use of in case of health analytics. Machine learning and deep learning are some concepts that have made a hype these days. Rather than just relying on the set of pre-programmed rules, machine learning is much more than that. The machine learning models are fed data as well as interaction and these can be made use of to develop the own sets of rules along with classifiers. There are much applications of machine learning in the healthcare sector and all of these have to be analysed.

The main research questions are as follows:

1. How is machine learning aiding in healthcare analytics?

2. What are the different ethical issues that can arise when machine learning is made use of extensively in healthcare analytics?

The research will help the readers to understand the significance of machine learning. It will be of great help to the healthcare sector professionals to think of arenas where they can apply machine learning. They can be guided by the study to choose between different applications of machine learning in their organization. The study can be helpful to other researchers as well who want to carry out research in this field. With machine learning, plans as well as providers are able to identify the hidden risks factors and identify the gaps in patient care. Thus, the study will be of great help to students wanting to research on this topic and as well as to the healthcare organizations those who want to invest on machine learning.

Artificial Intelligence (AI) is about the imitation of human intelligence in machines which are programmed to function just like humans and imitate their actions. This term in general is also applicable to machines that exhibit traits linked with human mind involving learning as well as problem-solving. The main trait of AI is its ability to both rationalize as well as take specific actions that have the best chance of achieving a certain goal. As per the research Daugherty and Wilson (2018), AI is dependent on the norm that human intelligence is defined in such a way that a machine can easily imitate it as well as execute the various tasks. The goals of artificial intelligence consist of learning, perception as well as reasoning.

With the advancement of technology, the earlier benchmarks that provided definition of artificial intelligence have become outdated. For instance, the machines calculating the basic functions or recognizing text by means of optical character recognition are no more taken into consideration to embody AI. AI is continuously evolving for benefitting various different industries. Machines are wired making use of cross-disciplinary approach that is based on mathematics, psychology, computer science and much more.

Literature Review

As per the research done by Canhoto and Clear (2020), AI can be divided into weak as well as strong. Weak AI systems embody a system that is designed for carrying out a specific job. These include video games, personal assistants as Apple’s Siri and others. On the other hand, strong AI systems are those which carry on those tasks that are considered to be human-like. These tend to be more complicated or rather complex systems. Strong AI are programmed to take hold of situations in which they are required to solve problems without having any person’s intervention. These types of systems can be found in applications such as self-driving cars. As it is still in the stage of inception,

AI has come under the scrutiny from both scientists as well as the public (Bartoletti 2019). One of the common notions is that machines in near future will be so highly developed that humans will not be able to keep up with these and they will certainly take off on their own, restructuring themselves at an exponential rate.

Machine Learning (ML) is the emerging branch of the computational algorithms, which are designed for emulating the human intelligence by the learning from surroundings environment. Those are measured the working horse in this new and advance era of big data. the techniques are based on the ML have been successfully applied in various fields ranging from computer vision, finance, pattern recognition, entertainment, computational biology, spacecraft to the medical application and biomedical. Machine Learning is a study of the methods and tools, which identifies the patterns in the various data. The patterns can be used for increasing the understandings of the present world or making the prediction about future. According to Wiens and Shenoy (2018), Machine Learning draws on the concept from multiple fields, which includes the statistics, optimization and computer science.

At the core, the issues with machine learning can be formulated as the optimization issues with the context of dataset. In this setting, the aim is to find out the model, which explains the data in a better way. While multiple different types of machine learning are there, most of the application falls into three categories such as reinforcement learning, supervised and unsupervised learning.

According to Wang et al. (2017), Machine Learning has the capability to learn from the data that the user provides. As the new data is provided by the user, the efficiency and the accuracy of the model for making the decisions for improving with the subsequent training. The powerful utility of the Machine Learning algorithm is the ability for automating the various decision-making the tasks. This always frees up much time for the developers for using the time for more use in the production. Machine learning is responsible to cut the time and the workload. By automating the things, the machine learning helps the business in their hard work also this helps the business to think creatively. Machine learning has the wide range of the applications. It means that the organizations can apply the machine learning on various major fields.