Robust Methodology Design to Predict Opioid Overdose System based on AI Assisted Deep Learning Principles

In this research, we present a deep learning approach for opioid toxicity prediction in patients based on electronic health records (E-HR) data using Modified Deep Gated Recurrent Unit (GRU) networks. To address the vanishing gradient issue, a recurrent neural network, the GRU model, is used to capture long term dependencies. This research to evaluate different model dimensions (16/32/64) and layer counts (2-4), and arrive at an architecture consisting of two GRU layers (32 units), and a sigmoid function as the output layer. We integrate an attention mechanism into the GRU model to improve feature prioritizing and boost performance. Datasets considered are from the State-wide Planning and Research Cooperative System (State-PARCS) and the Electronic Health Record (E-HR) of the trustable 3rd party source. The model proposed attains an overall accuracy of 98.36%, precision of 97.88%, recall of 98.29%, and an F1 Score of 97.70%. Our findings show that the model outperforms current Medicaid programs at predicting overdose risk and can be used to rapidly target interventions. The study brings out the importance of incorporating the risk assessment criteria into a more proactive healthcare system for patients with opioid dependency to achieve individual treatment outcomes that are optimized.