Combating the Problems of the Necessity of Continuous Learning in Medical Datasets of Type 2 Diabetes

There are two major problems that researchers must contend with when dealing with machine learning in the medical field. The first being the ever-changing nature of what is considered good practice, and the lack of available data to train. The change in opinion on what is considered good practice requires an ongoing effort to update the machine learning models. This requires a concept called continual learning, which requires that researchers must combat against the problem of a model forgetting previously learned information and the balancing of bigger classes and newer smaller classes. Usually, when new information is introduced, a model must be retrained, which threatens the previously gained knowledge. The training is then difficult because of the lack of data. However, when dealing with medications they can become irrelevant to use. When such things happen when dealing with the standard machine learning models, the entire model needs to be retrained in order to remove the specific medication. This causes even more difficulties, because patient data are heavily protected and there is the chance that the dataset will not be available for training again in the future. While most papers focus on medical imaging and diagnosis, medicine does not end with diagnosis. We have an elderly population that is growing and not enough doctors are are available. To have everyone be able to see specialists, or even a doctor, is becoming even harder. To combat these issues, we need to have models in use that we can update continuously to help bridge the gap of care. We propose a method that can be trained continuously in order to easily remove outdated medications.