Machine Learning
Machine learning (ML) is an automated process that detects patterns in data to learn tasks. It improves performance through experience. Key ML approaches/concepts include:
Deep Learning: This is a subset of ML that utilizes multilayered neural networks to learn from large amounts of data.
Supervised Learning: In this learning style, algorithms learn from a training dataset where the correct output (label) is already known.
Unsupervised Learning: In this learning style, algorithms detect hidden patterns in data where the output is unknown. This is highly useful for identifying patient clusters.
Classification: This is a prediction technique where models predict a category. An example is predicting if a patient is "high risk" versus "low risk" for readmission.
Regression: This is a prediction technique where models predict a continuous numerical value. An example is predicting a patient's specific length of stay.
Validation: This refers to the necessity of using separate training, validation, and test datasets. This practice ensures a model is accurate and generalizable to new patients.