## Results  
### Domain Expert Reviews  
Domain expert reviews provide an impression of a visual interface's usability and identify pitfalls without having to perform a full-fledged user study [27]. To initially evaluate and improve ML-MEDIC, we asked three clinical researchers to be involved in an initial review. After a short introduction, each clinician was paired with the tool developer to use the tool and explore machine learning applications in the context of their research. Five additional clinical researchers were given a demonstration of the tool using data and methods to evaluate its potential to facilitate machine learning adoption. All clinicians were asked to (1) give comments regarding their data, research context, and overall impression of the tool, and (2) rate the tool (\"positively\", \"neither-nor\", or \"negatively\") with respect to ease of use, layout, and likeliness to use. The overall opinion of the domain experts was positive regarding ease of use, layout, and likeliness to use, including comments such as \"The general design is easy to follow, the tables and graphics are well done, and is useable without having to code\" and \"It runs very fast, and is something I could use when collaborating with colleagues who have limited machine learning experience or are just getting started.\" Some experts explicitly appreciated the collaborative aspect and the compute power of ML-MEDIC implemented in the Precision Medicine Platform and liked not having to worry about installation or reusability. Many experts also appreciated the open-source nature of the code, with comments such as \"this tool is easy enough to use and has the methods I need, to perform analyses without licensed tools like SAS or STATA\" and \"This is perfect for teaching machine learning\". Case Study 1: Evaluate the Predictive Power of Various Machine Learning Methods to Predict Cardiovascular Risk  
In 2013, the American College of Cardiology (ACC) and the American Heart Association (AHA) published a Cox proportional hazards model to predict patient cardiovascular (CV) risk in primary care. This model is one of the first cardiovascular risk models to include separate models based on both gender and race, comprised of a specific Cox proportional hazards equation for African American Men and African American women, Caucasian Men and Caucasian Women. Since the model's release, the use of different statistical methods and cross-validation techniques have shown the potential to increase the accuracy of risk estimates. Collaborating with a data scientist, biostatistician, and cardiologist, we used pair analytics methodology to conduct this study, in which a domain expert and a tool developer collaboratively analyzed the data [28]. The overall analysis session lasted three hours. Given research suggesting net elastic models with cross-validation showed improved accuracy for predicting cardiovascular disease risk, the cardiologist wanted to investigate the performance of a random forest model and GBM compared to elastic net, using 10-fold, repeated, cross-validation. Using the tabular views of the data, each expert could easily see summary statistics of cases and controls (Figure 5). Setting each model and the cross-validation parameters, the input and response variables did not require any coding expertise. The total compute time to run all three models was less than ten minutes, which allowed all collaborators to reactively compute and recompute various models. Using the ROC curve, the clinician could easily compare each model's predictive performance to the performance of the elastic net, and the accuracy table could be used to numerically evaluate the performance. The random forest model resulted in the highest performance of the three models (Figure 3). Case Study 2: Determine Dietary Factors Important in Predicting Congestive Heart Failure (CHF), and Whether They are Significant for Predicting CHF Risk  
We collaborated with a cardiologist with experience in clinical research and data science to conduct case study 2.