  Machine-assisted treatment recommendations hold a promise to reduce physician
time and decision errors. We formulate the task as a sequence-to-sequence
prediction model that takes the entire time-ordered medical history as input,
and predicts a sequence of future clinical procedures and medications. It is
built on the premise that an effective treatment plan may have long-term
dependencies from previous medical history. We approach the problem by using a
memory-augmented neural network, in particular, by leveraging the recent
differentiable neural computer that consists of a neural controller and an
external memory module. But differing from the original model, we use dual
controllers, one for encoding the history followed by another for decoding the
treatment sequences. In the encoding phase, the memory is updated as new input
is read; at the end of this phase, the memory holds not only the medical
history but also the information about the current illness. During the decoding
phase, the memory is write-protected. The decoding controller generates a
treatment sequence, one treatment option at a time. The resulting dual
controller write-protected memory-augmented neural network is demonstrated on
the MIMIC-III dataset on two tasks: procedure prediction and medication
prescription. The results show improved performance over both traditional
bag-of-words and sequence-to-sequence methods.
