Domain Knowledge Inclusive Monotonic Neural Network Guides Patient-Specific Induction of General Anesthesia Dosing

Postinduction hypotension is a well-known risk factor for adverse postoperative outcomes. Anesthesiologists estimate anesthetic dosages based on a patient's chart and domain knowledge. Machine learning is increasingly applied in predicting postinduction hypotension, with neural networks providing a robust and accurate approach to model complex relationships. This study aims to use machine learning to suggest anesthetic doses, both generalized to an average patient population and personalized for specific patients, incorporating domain knowledge into the modeling process.

In this study, postinduction hypotension is defined as a mean arterial pressure (<65 mm Hg) occurring during the first 10 minutes after anesthesia induction. The dataset includes 201,000 patient records, after exclusion criteria, containing clinical data, medication history, procedure descriptions, and anesthetic dosages for fentanyl and propofol. Several classification algorithms were implemented to model postinduction hypotension, and likelihood calculations were made by fixing values of fentanyl and propofol dosages to assess patient risk.

Gradient boosting and neural network models demonstrated the highest performance. However, these models did not account for domain experts' knowledge that anesthetic dosage and postinduction hypotension have a monotonically increasing relationship. To address this limitation, we developed a monotonic neural network (MNN), which integrates this domain knowledge. The models' results are presented through heatmaps, illustrating the likelihood of postinduction hypotension for both average and specific patients, with the MNN generating smoother, more plausible predictions compared to traditional models.

We successfully predicted postinduction hypotension using the MNN, achieving performance comparable to existing methods. This model, by encoding clinically relevant monotonic relationships, provides anesthesiologists with a tool to assist in patient-specific fentanyl and propofol dosages, improving both the interpretability and clinical relevance of anesthetic dosing strategies.