Learning medical triage by using a reinforcement learning approach

Many emergency departments are today suffering from a overcrowding of people seeking care. The first stage in seeking care is being prioritised in different orders depending on symptoms by a doctor or nurse called medical triage. This is a cumbersome process that could be subject of automatisation. This master thesis investigates the possibility of using reinforcement learning for performing medical triage of patients. A deep Q-learning approach is taken for designing the agent for the environment together with the two extensions of using double Q-learning and a duelling network architecture. The agent is deployed to train in two different environments. The goal for the agent in the first environment is to ask questions to a patient and then decide, when enough information has been collected, how the patient should be prioritised. The second environment makes the agent decide which questions should be asked to the patient and then a separate classifier is used with the information gained to perform the actual triage decision of the patient. The training and testing process of the agent in the two environments reveal difficulties in exploring the environment efficiently and thoroughly. It was also shown that defining a reward function for the environments that guides the agent into asking valuable questions and learninga stopping condition for asking questions is a complicated task. Suitable future work is discussed that would, in combination with the work performed in this paper, create a better reinforcement learning model that could potentially show more promising results in the task of performing medical triage of patients.