arXiv Open Access 2018

Model-Based Reinforcement Learning for Sepsis Treatment

Aniruddh Raghu Matthieu Komorowski Sumeetpal Singh

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Abstrak

Sepsis is a dangerous condition that is a leading cause of patient mortality. Treating sepsis is highly challenging, because individual patients respond very differently to medical interventions and there is no universally agreed-upon treatment for sepsis. In this work, we explore the use of continuous state-space model-based reinforcement learning (RL) to discover high-quality treatment policies for sepsis patients. Our quantitative evaluation reveals that by blending the treatment strategy discovered with RL with what clinicians follow, we can obtain improved policies, potentially allowing for better medical treatment for sepsis.

Topik & Kata Kunci

cs.LG cs.AI stat.ML

Penulis (3)

Aniruddh Raghu

Matthieu Komorowski

Sumeetpal Singh

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Raghu, A., Komorowski, M., Singh, S. (2018). Model-Based Reinforcement Learning for Sepsis Treatment. https://arxiv.org/abs/1811.09602

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Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓