GCU Machine Learning Discussion

I’m working on a computer science discussion question and need support to help me learn.

Please reply toeach DQ with 100-150 answer each. Thanks

(1) Swapenpreet

Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. Recommendation engines are a common use case for machine learning. Other popular uses include fraud detection, spam filtering, malware threat detection, business process automation (BPA) and predictive maintenance. Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Machine learning has become a significant competitive differentiator for many companies. Machine learning also plays a key role in many health-related realms, including the development of new medical procedures, the handling of patient data and records and the treatment of chronic diseases. Modernizing workflows, facilitating access to clinical data and improving the accuracy and flow of health information are just some of the benefits which come from machine learning in healthcare (Bharadwaj et al., 2021).

Perry

Reference:

Bharadwaj, H. K., Agarwal, A., Chamola, V., Lakkaniga, N. R., Hassija, V., Guizani, M., & Sikdar, B. (2021). A Review on the Role of Machine Learning in Enabling IoT Based Healthcare Applications. IEEE Access, Access, IEEE, 9, 38859–38890. https://doi-org.lopes.idm.oclc.org/10.1109/ACCESS….

(2) Bobbie

Machine learning technology is an artificial intelligence (AI) application that utilizes data and algorithms to imitate how humans learn and progressively enhance its accuracy. It offers systems the capacity to automatically learn and get better from experience without being precisely programmed (Watson et al., 2019). Machine learning involves developing computer programs that access data and utilize it to learn for themselves. It purposes to improve the performance of a system when managing new data for a particular setting (Watson et al., 2019). To achieve this purpose effectively and efficiently, it relies heavily on computer science and statistics.

Machine learning technology is widely being applied in healthcare to help patients and clinicians in various ways. It is mainly used in clinical decision support, automating medical billing, and developing clinical care guidelines (Callahan & Shah, 2017). For instance, the first medical machine learning algorithm was developed to foretell acute toxicities in patients on radiation therapy for neck and head cancers. In addition, machine learning technology is applied to electronic health records to produce practical insights, such as enhancing patient risk score systems, predicting disease onset, and restructuring hospital operations (Callahan & Shah, 2017). Machine learning has a wide array of potential applications in research and clinical trials. Its predictive analysis identifies potential clinical trial subjects, which helps researchers draw with a pool from a wide array of data points.

Machine learning technology is associated with several benefits in healthcare. It helps in identifying individuals with chronic conditions that are undiagnosed or misdiagnosed. Besides, it helps predict chronic illnesses in patients and provides patient-specific preventive measures (Watson et al., 2019). Machine learning has been beneficial in drug discovery and manufacturing, particularly in the early stage of the drug discovery process. It also helps maintain smart health records and eases the maintenance of health records by saving time, finances, and workforce.

References

Callahan, A., & Shah, N. H. (2017). Machine learning in healthcare. In Key Advances in Clinical Informatics (pp. 279-291). Academic Press. https://doi.org/10.1016/B978-0-12-809523-2.00019-4

Watson, D. S., Krutzinna, J., Bruce, I. N., Griffiths, C. E., McInnes, I. B., Barnes, M. R., & Floridi, L. (2019). Clinical applications of machine learning algorithms: beyond the black box. Bmj, 364. https://doi.org/10.1136/bmj.l886

(3) Venessa

Machine Learning is a utilization of artificial intelligence (AI) that gives systems the capacity to consequently learn and improve without being unequivocally customized. Machine Learning centers around the advancement of PC programs that access information and use it for continuous learning. It processes and finds patterns in large data sets to enable decision-making. According to expert.ai, “The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly” (Expert.AI, 2021).

Machine Learning assumes a critical part in numerous healthcare related domains, including the improvement of new operations, the treatment of patient information and records and the therapy of ongoing infections.

Machine learning in health informatics can smooth out recordkeeping, including electronic health records (EHRs). Utilizing AI to improve EHR the staff can improve patient consideration, decrease medical services and regulatory expenses, and enhance tasks.

Machine learning in algorithms can likewise make EHR the board frameworks simpler to use for doctors by giving clinical choice help, robotizing image investigation, and coordinating telehealth advances.

Thanks,

Venessa

Expert.AI. (2021, May 26). What is the Definition of Machine Learning? Retrieved from

https://www.expert.ai/blog/machine-learning-definition/