Contextual Care Protocol using Neural Networks and Decision Trees
This addresses the need for scalable, automated healthcare protocols for medical practitioners, though it appears incremental as it builds on existing neural network and decision tree methods.
The paper tackled the problem of automating healthcare decision-making by proposing a hybrid model that combines neural networks and decision trees to create a self-adapting contextual care protocol, which adapts based on expert corrections to improve early and effective healthcare delivery.
A contextual care protocol is used by a medical practitioner for patient healthcare, given the context or situation that the specified patient is in. This paper proposes a method to build an automated self-adapting protocol which can help make relevant, early decisions for effective healthcare delivery. The hybrid model leverages neural networks and decision trees. The neural network estimates the chances of each disease and each tree in the decision trees represents care protocol for a disease. These trees are subject to change in case of aberrations found by the diagnosticians. These corrections or prediction errors are clustered into similar groups for scalability and review by the experts. The corrections as suggested by the experts are incorporated into the model.