Automatic Early Risk Detection of Possible Medical Conditions for Usage Within an AMI-System

摘要

Using hyperglycemia as an example, we present how Bayesian networks can be utilized for automatic early detection of a person's possible medical risks based on information provided by unobtrusive sensors in their living environments. The network's outcome can be used as a basis on which an automated AMI-system decides whether to interact with the person, their caregiver, or any other appropriate party. The networks' design is established through expert elicitation and validated using a half-automated validation process that allows the medical expert to specify validation rules. To interpret the networks' results we use an output dictionary which is automatically generated for each individual network and translates the output probability into the different risk classes (e.g., no risk, risk).