The use of machine learning in Healthcare has the potential to improve
patient outcomes as well as broaden the reach and affordability of Healthcare.
The history of other application areas indicates that strong benchmarks are
essential for the development of intelligent systems. We present Personal
Health Interfaces Leveraging HUman-MAchine Natural interactions (PhilHumans), a
holistic suite of benchmarks for machine learning across different Healthcare
settings - talk therapy, diet coaching, emergency care, intensive care,
obstetric sonography - as well as different learning settings, such as action
anticipation, timeseries modeling, insight mining, language modeling, computer
vision, reinforcement learning and program synthesis

利用机器学习在医疗保健中的应用具有改善患者预后、扩大医疗保健的覆盖范围和可负担性的潜力。我们提出了 Personal Health Interfaces Leveraging HUman-MAchine Natural interactions（PhilHumans），它是一个全面的机器学习基准套件，涵盖不同医疗保健领域（如语言治疗、膳食指导、急诊护理、重症护理、产科超声）以及不同的学习设置，例如动作预测、时间序列建模、洞见挖掘、语言建模、计算机视觉、强化学习和程序合成。