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
@article{arxiv.2405.02770,
title = {PhilHumans: Benchmarking Machine Learning for Personal Health},
author = {Vadim Liventsev and Vivek Kumar and Allmin Pradhap Singh Susaiyah and Zixiu Wu and Ivan Rodin and Asfand Yaar and Simone Balloccu and Marharyta Beraziuk and Sebastiano Battiato and Giovanni Maria Farinella and Aki Härmä and Rim Helaoui and Milan Petkovic and Diego Reforgiato Recupero and Ehud Reiter and Daniele Riboni and Raymond Sterling},
journal= {arXiv preprint arXiv:2405.02770},
year = {2024}
}