HIM 650 Topic 8 DQ 1


Machine learning, also known as artificial intelligence, has significantly improved the outcome of medical treatments in recent years. This article will summarize the differences between supervised and unsupervised machine learning and discuss specific examples of how machine learning is used in today’s medical field.

Supervised and unsupervised machine learning are both used in health care. Supervised machine-learning programs tend to be used in health care as a way to spot trends early, such as identifying diseases before patients exhibit any symptoms. Unsupervised machine-learning programs use past data to find undiscovered patterns, which could then be investigated further by human analysts.

supervised and unsupervised learning are two types of machine learning. Supervised learning is when a robot or computer is programmed to do something until it learns to do on its own. Unsupervised learning is used when there is too much data and people doesn’t know what they want the robot/computer to choose. It has been used in health care to collect data from social media sites such as Twitter.

Supervised machine learning is considered to be the most common and basic form of machine learning. It uses labeled data as an explicit tutor, where labels are input during training to determine what the algorithm needs to learn. Unsupervised machine learning, on the other hand, does not use explicit labels. It works in a manner that allows the algorithm to perform self-learning on un-labeled datasets. In health care, supervised machine learning algorithms are used to detect trends based on health data collected by various devices (i.e., heart rate data) or mobile applications (i.e., step counting applications). By analyzing historical health data sets and various device logs, a supervised algorithms can predict future symptoms and occurrences of diseases/disorders and recommend appropriate treatment regimens to ensure overall health of individual….

Machine learning is the technology of artificial intelligence (AI). It makes use of big data, algorithms and computational methods to allow a computer to learn from interaction with humans and from data examples. This technology has been used in various areas and such as Google, search engines, internet security systems, ATMs, self-driving cars, digital assistants, image recognition, AI-based apps and so forth. The procedure entails an end user indicating how accurate an algorithm is by identifying its successes and failures. Machine learning predicts complex outcomes instead of simply searching through data for patterns. In health care, the process can be implemented in a number of ways such as informing physicians when they make errors and aiding radiologists in detecting tumors using MRI scans.

In supervised machine learning, humans provide the machine with information about the necessary aspects of a process. Humans must define two sets of information, reliability and valid signals.

Supervised and unsupervised machine learning have many differences, but at their most basic level, they have one same goal: to discover patterns in a set of data. All machine learning practitioners use algorithms to take in a set of data and determine how best to render the information or outcomes about that data. However, supervised learning uses the feedback of a human to adjust the model based on known results, whereas unsupervised learning searches for correlations without human intervention.

Machine learning is a set of algorithms that can learn to make predictions from a data set, without being told the criteria on which they should base those predictions. Machine learning is used in medical records to predict patients that are at risk of having a medical condition or finding a cure for an illness.





Discuss the difference between supervised and unsupervised machine learning. Provide examples of how machine learning is used in health care.

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