Year: 2024
Type: preprint
Abstract: 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
Source: arXiv (Cornell University)
Authors: Liventsev, Vadim, Kumar, Vivek, Susaiyah, Allmin Pradhap Singh, Wu, Zixiu, Rodin, Ivan, Yaar, Asfand, Balloccu, Simone, Beraziuk, Marharyta, Battiato, Sebastiano, Farinella, Giovanni Maria, Härmä, Aki, Helaoui, Rim, Petkovic, Milan, Recupero, Diego Reforgiato, Reiter, Ehud, Riboni, Daniele, Sterling, Raymond
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funder: European Commission
OpenAlex ID: W4396718167