Machine learning with optimization to create medicine intake schedules for Parkinson's disease patients.

This paper presents a solution for creating individualized medicine intake schedules for Parkinson's disease patients. Dosing medicine in Parkinson's disease is a difficult and a time-consuming task and wrongly assigned therapy affects patient's quality of life making the disease more uncomfortable. The method presented in this paper may decrease errors in therapy and time required to establish a suitable medicine intake schedule by using objective measures to predict patient's response to medication. Firstly, it demonstrates the use of machine learning models to predict the patient's medicine response based on their state evaluation acquired during examination with biomedical sensors. Two architectures, a multilayer perceptron and a deep neural network with LSTM cells are proposed to evaluate the patient's future state based on their past condition and medication history, with the best patient-specific models achieving R2 value exceeding 0.96. These models serve as a foundation for conventional optimization, specifically genetic algorithm and differential evolution. These methods are applied to find optimal medicine intake schedules for patient's daily routine, resulting in a 7% reduction in the objective function value compared to existing approaches. To achieve this goal and be able to adapt the schedule during the day, reinforcement learning is also utilized. An agent is trained to suggest medicine doses that maintain the patient in an optimal state. The conducted experiments demonstrate that machine learning models can effectively model a patient's response to medication and both optimization approaches prove capable of finding optimal medicine schedules for patients. With further training on larger datasets from real patients the method has the potential to significantly improve the treatment of Parkinson's disease.