Summary of Philhumans: Benchmarking Machine Learning For Personal Health, by Vadim Liventsev et al.
PhilHumans: Benchmarking Machine Learning for Personal Health
by Vadim Liventsev, Vivek Kumar, Allmin Pradhap Singh Susaiyah, Zixiu Wu, Ivan Rodin, Asfand Yaar, Simone Balloccu, Marharyta Beraziuk, Sebastiano Battiato, Giovanni Maria Farinella, Aki Härmä, Rim Helaoui, Milan Petkovic, Diego Reforgiato Recupero, Ehud Reiter, Daniele Riboni, Raymond Sterling
First submitted to arxiv on: 4 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces PhilHumans, a comprehensive suite of benchmarks for machine learning in Healthcare. The benchmarks aim to improve patient outcomes by developing intelligent systems that can operate effectively across various healthcare settings, including talk therapy, diet coaching, emergency care, and more. The PhilHumans framework includes different learning settings, such as action anticipation, timeseries modeling, and language modeling, which are essential for the development of robust machine learning models in Healthcare. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PhilHumans is a set of benchmarks that can be used to develop intelligent systems in Healthcare. These systems have the potential to improve patient outcomes by providing personalized care and making healthcare more accessible and affordable. The benchmarks cover various healthcare settings and different types of machine learning tasks, such as timeseries modeling and language modeling. |
Keywords
» Artificial intelligence » Machine learning
