In recent years, researchers have been developing machine learning algorithms for an increasingly wide range of purposes. This includes algorithms that can be applied in healthcare settings, for instance helping clinicians to diagnose specific diseases or neuropsychiatric disorders or monitor the health of patients over time.
As reported byTechXplore., researchers at Massachusetts Institute of Technology (MIT) and Massachusetts General Hospital have recently carried out a study investigating the possibility of using deep reinforcement learning to control the levels of unconsciousness of patients who require anaesthesia for a medical procedure.
Gabriel Schamberg and his colleagues developed a deep neural network and trained it to control anaesthetic dosing using reinforcement learning within a simulated environment. They specifically focused on the dosage of Propofol, a medication that decreases people’s level of consciousness and is commonly used to perform general anaesthesia or sedation on patients who are undergoing medical procedures.
The researchers trained the neural network they developed on simulated patient data, which was generated based on pharmacokinetic/pharmacodynamic models with randomized parameters. This ultimately allowed them to account for numerous patients with varying characteristics and features.