Salvato in:
| Autori principali: | , , , , , , , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2405.02770 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866929345029734400 |
|---|---|
| author | 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 |
| author_facet | 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 |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02770 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PhilHumans: Benchmarking Machine Learning for Personal Health 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 Machine Learning 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 |
| title | PhilHumans: Benchmarking Machine Learning for Personal Health |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.02770 |