Post-induction hypotension (PIH) occurs shortly after anesthesia induction and is related to several post-operative complications. Medications delivered during induction and maintenance of anesthesia are significantly related to PIH occurrence, which remains common due to the intricate nature of clinical factors. To enhance decision-making on anesthestic dosing, machine learning (ML) is proposed to predict the risk of PIH associated with specific anesthetic dosages. This study focuses on the development of a prediction model for PIH to support anesthesia decision-making. Trained on 320 cases from the VitalDB database, the model incorporates demographic data, vital signs, and medication dosing information. By including the dosage of propofol administered during the induction period as an input variable, the algorithm predicts PIH risk before induction, providing valuable insights into the safety of propofol dosage plans. The results were validated using nested cross-validation, achieving high performance (precision of 0.83 and recall of 0.84). Moreover, an advisory model demonstrates the potential for personalizing a safe propofol anesthetics range for an individual patient.

Cardiac output (CO) is a vital hemodynamic parameter that reflects the blood volume pumped by the heart per minute. A less-invasive way to estimate CO is by analyzing arterial blood pressure (ABP) waveforms. However, the relationship between CO and blood pressure is unknown. This study uses machine learning and feature engineering techniques to discover the relationship between CO and ABP. We apply the sparse identification non-linear dynamics (SINDy) algorithm to discover features. Additionally, we investigate the optimum number of cardiac cycles required for feature extraction to achieve the best performance. The proposed approach achieves clinically acceptable performance regarding radial limits of agreement (RLOA) and bias (RBias). Further, the proposed approach is validated on an external dataset. Finally, similarities to the Navier-Stokes equations are presented.