PhilHumans: Benchmarking Machine Learning for Personal Health

Understaffing has been consistently identified as the major challenge facing Healthcare today [7, 1, 2, 21, 55, 82, 97, 87, 124]. Automation tools that make use of Machine Learning (also known as Healthcare 4.0 [126]) have been consistently identified as crucial for reducing the workload of Healthcare professionals and improving the quality of care [5, 34, 44, 46, 78, 86, 94, 136]. In turn, the shortage of standard benchmarks has been consistently identified as a central roadblock for machine learning in Healthcare [27, 31, 49, 52, 59, 76, 81, 95, 110]. Whether it's ImageNet [32] in Computer Vision or GLUE [128] in natural language processing, benchmarks are a core research tool in mature applications of machine learning, enabling quantitative analysis of learning methodologies to guide and orient their development.