Fix GAMENet's Dynamic Memory - #1191
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GAMENet.forward() hardcoded prev_drugs = torch.zeros(...) instead of the
patient's actual drug history, making the graph-augmented Dynamic Memory
mechanism the model is named for permanently inert. Per the paper (Shang
et al., "GAMENet: Graph Augmented MEmory Networks for Recommending
Medication Combination," AAAI 2019, arXiv:1809.01852), the Dynamic
Memory's values (Eq. 6) should be [c_m^1; ...; c_m^{t-1}] -- each
previous visit's actual administered drugs, retrieved via temporal
attention (Eq. 7). That attention/retrieval math was already correct;
only the memory's content was wrong.
pyhealth.tasks.drug_recommendation already produces exactly the needed
field (drugs_hist: nested per-visit drug history, current/target visit
zeroed out) -- GAMENet just never consumed it (an unused batch_to_multihot
import was a leftover sign of the abandoned wiring).
Fix: require drugs_hist in the dataset schema (a silent zeros-fallback
would just reintroduce the same silently-wrong-results bug in a new
form), precompute a remap from drugs_hist's own input vocabulary to the
drugs label_vocab used by ehr_adj/ddi_adj/the Memory Bank (they are
tokenized independently), and build the real multi-hot prev_drugs tensor
from that remapped history.
Verified end-to-end on real hardware: unit tests (including two new
regression tests), a 30-patient stress test with variable visit counts,
and the actual documented example script trained against real synthetic
MIMIC-III data.
fbonc
approved these changes
Aug 20, 2026
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GAMENet.forward() hardcoded prev_drugs = torch.zeros(...) instead of the patient's actual drug history
require drugs_hist in the dataset schema, precompute a remap from drugs_hist's own input vocabulary to the drugs label_vocab used by ehr_adj/ddi_adj/the Memory Bank, and build the real multi-hot prev_drugs tensor from that remapped history