  To avoid serious diabetic complications, people with type 1 diabetes must
keep their blood glucose levels (BGLs) as close to normal as possible. Insulin
dosages and carbohydrate consumption are important considerations in managing
BGLs. Since the 1960s, models have been developed to forecast blood glucose
levels based on the history of BGLs, insulin dosages, carbohydrate intake, and
other physiological and lifestyle factors. Such predictions can be used to
alert people of impending unsafe BGLs or to control insulin flow in an
artificial pancreas. In past work, we have introduced an LSTM-based approach to
blood glucose level prediction aimed at "what if" scenarios, in which people
could enter foods they might eat or insulin amounts they might take and then
see the effect on future BGLs. In this work, we invert the "what-if" scenario
and introduce a similar architecture based on chaining two LSTMs that can be
trained to make either insulin or carbohydrate recommendations aimed at
reaching a desired BG level in the future. Leveraging a recent state-of-the-art
model for time series forecasting, we then derive a novel architecture for the
same recommendation task, in which the two LSTM chain is used as a repeating
block inside a deep residual architecture. Experimental evaluations using real
patient data from the OhioT1DM dataset show that the new integrated
architecture compares favorably with the previous LSTM-based approach,
substantially outperforming the baselines. The promising results suggest that
this novel approach could potentially be of practical use to people with type 1
diabetes for self-management of BGLs.
