Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning

• Main Authors: Kondrup, Flemming, Jiralerspong, Thomas, Lau, Elaine, de Lara, Nathan, Shkrob, Jacob, Tran, My Duc, Precup, Doina, Basu, Sumana
• Format: Journal Article
• Language: English
• Published: 05-10-2022
• Subjects: Computer Science - Learning
• DOI: 10.48550/arxiv.2210.02552

Mechanical ventilation is a key form of life support for patients with
pulmonary impairment. Healthcare workers are required to continuously adjust
ventilator settings for each patient, a challenging and time consuming task.
Hence, it would be beneficial to develop an automated decision support tool to
optimize ventilation treatment. We present DeepVent, a Conservative Q-Learning
(CQL) based offline Deep Reinforcement Learning (DRL) agent that learns to
predict the optimal ventilator parameters for a patient to promote 90 day
survival. We design a clinically relevant intermediate reward that encourages
continuous improvement of the patient vitals as well as addresses the challenge
of sparse reward in RL. We find that DeepVent recommends ventilation parameters
within safe ranges, as outlined in recent clinical trials. The CQL algorithm
offers additional safety by mitigating the overestimation of the value
estimates of out-of-distribution states/actions. We evaluate our agent using
Fitted Q Evaluation (FQE) and demonstrate that it outperforms physicians from
the MIMIC-III dataset.