Medi-Care AI: Predicting Medications From Billing Codes via Robust Recurrent Neural Networks
This work addresses a critical problem in healthcare informatics for improving medication management, but it is incremental as it builds on existing RNN methods with specific robustness enhancements.
The paper tackles the challenge of predicting a patient's medication classes from diagnostic billing codes, which are often incomplete or erroneous, by introducing a robust RNN framework that models contamination through decay mechanisms and noise injection, achieving effective results on real healthcare data.
In this paper, we present an effective deep prediction framework based on robust recurrent neural networks (RNNs) to predict the likely therapeutic classes of medications a patient is taking, given a sequence of diagnostic billing codes in their record. Accurately capturing the list of medications currently taken by a given patient is extremely challenging due to undefined errors and omissions. We present a general robust framework that explicitly models the possible contamination through overtime decay mechanism on the input billing codes and noise injection into the recurrent hidden states, respectively. By doing this, billing codes are reformulated into its temporal patterns with decay rates on each medical variable, and the hidden states of RNNs are regularised by random noises which serve as dropout to improved RNNs robustness towards data variability in terms of missing values and multiple errors. The proposed method is extensively evaluated on real health care data to demonstrate its effectiveness in suggesting medication orders from contaminated values.