  Objectives: To evaluate the consequences of the framing of machine learning
risk prediction models. We evaluate how framing affects model performance and
model learning in four different approaches previously applied in published
artificial-intelligence (AI) models.
  Setting and participants: We analysed structured secondary healthcare data
from 221,283 citizens from four Danish municipalities who were 18 years of age
or older.
  Results: The four models had similar population level performance (a mean
area under the receiver operating characteristic curve of 0.73 to 0.82), in
contrast to the mean average precision, which varied greatly from 0.007 to
0.385. Correspondingly, the percentage of missing values also varied between
framing approaches. The on-clinical-demand framing, which involved samples for
each time the clinicians made an early warning score assessment, showed the
lowest percentage of missing values among the vital sign parameters, and this
model was also able to learn more temporal dependencies than the others. The
Shapley additive explanations demonstrated opposing interpretations of SpO2 in
the prediction of sepsis as a consequence of differentially framed models.
  Conclusions: The profound consequences of framing mandate attention from
clinicians and AI developers, as the understanding and reporting of framing are
pivotal to the successful development and clinical implementation of future AI
technology. Model framing must reflect the expected clinical environment. The
importance of proper problem framing is by no means exclusive to sepsis
prediction and applies to most clinical risk prediction models.

Machine Learning;Artificial Intelligence;Machine Learning