FIG 1.

General overview of machine learning analyses. (A) ML analyses are capable of integrating a wide variety of data types. These include raw images or instrument traces, pathogen genomic sequences, data obtained from wet lab experiments, and information contained in the electronic health record. The latter encompasses clinical pathology results, free text notes, and structured data such as demographics, comorbidities, procedures, allergies, medication exposures, and hospital encounters. These inputs must be carefully cleaned and the relevant features extracted or engineered. Multiple validation checks are often necessary to ensure the data remain accurate after preprocessing. Next, the data are split into a training set used to define the model parameters and a remaining portion is held out for testing model performance. (B) There are three broad categories of ML analyses. The first two are supervised and unsupervised learning. In supervised learning, training data contains labels denoting the outcome of interest (i.e., antibiotic resistance phenotypes). The model trains on these data and then predicts the predefined outcomes of interest on test data. Unsupervised models are trained on data that does not contain labels for the outcome of interest. The model therefore searches on its own for relationships between variables and then predicts these relationships on unlabeled test data. A typical use for unsupervised learning involves clustering high dimensional data and outlier detection. The final category of ML analyses are reinforcement learning models. These models comprise an “agent” which interacts with its environment over time. The state of the environment is provided to the agent, and the agent then chooses an action, e.g., an antibiotic treatment choice, from a set of available options. It then assesses the impact of that action on the environment through a reward function. The purpose of the reinforcement learning agent is to learn a set of actions for different states (i.e., a “policy”) that maximizes the cumulative reward.