Machine learning in healthcare
Machine learning is a field of computer science in which computational machines perform tasks without being given a set of instructions regarding how to perform that task. These machines are represented as models or algorithms with certain attributes to describe them. These attributes include some performance metric in which the model is judged from, a knowledge base which includes everything the model knows or has seen before, and a set of tasks that the model is performing, . In machine learning, a machine is said to learn if its performance with respect to this metric has improved. In this sense, there is some notion of what models are better than others when solving particular problems. The primary goal of any machine learning algorithm or model is to be able to analyze some initial set of data, grow its knowledge base according to this analysis, and be able to make accurate and relevant predictions about unseen data in the future. Problems that tend to be ideal candidates for machine learning are regression or classification problems in which the goal is to observe and identify trends and patterns in data and be able to extend these observations to unseen data. There are many different types of learning which are designed to target different types of problems, some of which include reinforcement learning, deep learning, and self learning. Some examples of this may include using a camera-based agent to distinguish apples from oranges, predicting the trajectory of a company's sales given the sales patterns over the past few years, or even predicting whether someone has a certain disease based on what symptoms they are exhibiting or certain family history.
Contents
Use of Machine Learning in Healthcare
Limitations
While machine learning
Past Use
Future Use
Machine learning is becoming particularly relevant in healthcare. Technology excels at identifying patterns and behaviors with information that it takes in, so this is very helpful in applications such as identifying the presence of certain diseases, creating documentation on patient records, providing medical information through chat-bots, and even performing surgeries.[1]
Ethical Issues
Use of Personal Data
Lack of Transparency
Talk about interpretability and what that means about machine learning algorithms in general. One concept relevant to machine learning models is model interpretability. A model the has a high level of interpretability has features that
