Informing patients that they are at high risk for serious complications of viral infection increases vaccination rates.

For many vaccine-preventable diseases like influenza, vaccination rates are lower than optimal to achieve community protection. Those at high risk for infection and serious complications are especially advised to be vaccinated to protect themselves. Using influenza as a model, we studied one method of increasing vaccine uptake: informing high-risk patients, identified by a machine learning model, about their risk status.

Prediction of progression from pre-diabetes to diabetes: Development and validation of a machine learning model.

Aims: Identification, a priori, of those at high risk of progression from pre-diabetes to diabetes may enable targeted delivery of interventional programmes while avoiding the burden of prevention and treatment in those at low risk. We studied whether the use of a machine-learning model can improve the prediction of incident diabetes utilizing patient data from electronic medical records.

Methods: A machine-learning model predicting the progression from pre-diabetes to diabetes was developed using a gradient boosted trees model.