  Objective: Machine learning algorithms are now widely used in predicting
acute events for clinical applications. While most of such prediction
applications are developed to predict the risk of a particular acute event at
one hospital, few efforts have been made in extending the developed solutions
to other events or to different hospitals. We provide a scalable solution to
extend the process of clinical risk prediction model development of multiple
diseases and their deployment in different Electronic Health Records (EHR)
systems.
  Materials and Methods: We defined a generic process for clinical risk
prediction model development. A calibration tool has been created to automate
the model generation process. We applied the model calibration process at four
hospitals, and generated risk prediction models for delirium, sepsis and acute
kidney injury (AKI) respectively at each of these hospitals.
  Results: The delirium risk prediction models achieved area under the
receiver-operating characteristic curve (AUROC) ranging from 0.82 to 0.95 over
different stages of a hospital stay on the test datasets of the four hospitals.
The sepsis models achieved AUROC ranging from 0.88 to 0.95, and the AKI models
achieved AUROC ranging from 0.85 to 0.92.
  Discussion: The scalability discussed in this paper is based on building
common data representations (syntactic interoperability) between EHRs stored in
different hospitals. Semantic interoperability, a more challenging requirement
that different EHRs share the same meaning of data, e.g. a same lab coding
system, is not mandated with our approach.
  Conclusions: Our study describes a method to develop and deploy clinical risk
prediction models in a scalable way. We demonstrate its feasibility by
developing risk prediction models for three diseases across four hospitals.
