## 2 Materials and Methods
### Tool Design  
The overall design goal for ML-MEDIC was to provide a point-and-click user-friendly tool that could facilitate the adoption of machine learning and data science in medical settings. We integrated visual and reactive computational approaches into the design to create an easy-to-use application that can engage domain experts, independent of their coding expertise. We chose R/Shiny as a framework to implement ML-MEDIC because of its open-source nature, interactive capabilities, and ability to re-compute calculations as necessary based on user input [11]. We deployed ML-MEDIC in a cloud environment for accessibility and reproducibility. Cloud-based environments have recently been developed to improve data access, sharing, and collaboration, as well as provide means for managing computational load and challenges associated with storing and analyzing big data [12; 13]. We chose to deploy ML-MEDIC in an Amazon Web Services cloud environment built by the American Heart Association (AHA) for Precision Cardiovascular Medicine. The Precision Medicine Platform, a secure, Health Insurance Portability and Accountability Act (HIPPA)- and Federal Risk and Authorization Management Program (FedRAMP)-certified cloud-based ecosystem, is an interactive environment for facilitating data sharing, collaboration, and power computing. The platform is comprised of common machine learning tools and biomedical datasets, which enables researchers to easily store, analyze, and build analysis pipelines collaboratively without worrying about data security through the use of workspaces. Workspaces enable users to share data privately or with the public, perform analytics, and securely access and reuse analytic pipelines virtually [14]. Given input from clinical domain experts, we chose to focus on supervised machine learning methods for ML-MEDIC due to their ability to provide more clinically relevant results, and the domain experts identified noted supervised models as some of the models they most frequently rely on for their data analysis. However, the ML-MEDIC was designed in a modular manner to facilitate the addition of other models, including unsupervised ones. The machine learning algorithms were implemented using Caret, with parallelized options to optimize the computing time and improve reactivity [15; 16; 17; 18]. We deployed ML-MEDIC in the Precision Medicine Platform to limit challenges associated with reusing or installing the tool on multiple computers, providing secure internet access to the tool, and allowing users to collaborate in real time (Figure 1). The AHA's HIPPA- and FedRAMP-certified Precision Medicine Platform facilitates reusability and reproducibility by creating a virtual environment and a workspace that segregates the computation from the underlying hardware and host's operating system [14; 19]. The platform currently uses Chef to ensure the correct versioning and configurations are applied consistently at any scale [20]. The cloud-based ecosystem also includes a data marketplace for data contributors to securely share datasets and analytics with the public, even if data is codified in Data Use Agreements (DUAs). A variety of datasets are accessible through the platform, and users can share analytic pipelines, results, and code through traditional methods such as GitHub, as well as the through the platform itself [21; 22; 23]. To create an \"easy-to-use\" interface, we structured the menu design to reflect the flow of a typical predictive analysis to align the interface design with the steps one would expect to implement when building a predictive model [5; 24]. We identified these steps to be: (1) loading data; (2) building one or multiple models for comparison, including the ability to build the same model on two different datasets; (3) setting optional training and control parameters; (4) testing and evaluation (Figures 2 and 3). Figure 1: The Precision Medicine Platform.