Intraoperative hypotension (IOH) is common during surgical procedures. It may be caused by anesthesia drugs, underlying comorbidities of the patient such as heart failure, or by the surgical procedure. There are strong associations between IOH and postoperative organ complications, thus the ability to predict IOH to support the clinician is an attractive prospect. In this presentation, the prediction of hypotension during general anesthesia using physiological signal is investigated. Recently, some research has been focused on trying to predict IOH events occurring during surgeries using machine learning techniques [1]. However, this methodology has been subject to some questioning [2], [3]. Especially, the database seems to be biased, and the evaluations are not compared to a relevant baseline. A new methodology addressing the arisen questions is proposed here. Exploratory investigations are illustrated using the open source VitalDB database [4] and standard machine learning algorithms. In addition, the explainability of the prediction is studied to provide more information to the anesthesiologist. References [1] F. Hatib, Z. Jian, S. Buddi, C. Lee, J. Settels, K. Sibert, J. Rinehart, and M. Can-nesson, “Machine-learning Algorithm to Predict Hypotension Based on High-fidelityArterial Pressure Waveform Analysis,”Anesthesiology, vol. 129, pp. 663–674, Oct.2018. [2] J. Enevoldsen and S. T. Vistisen, “Performance of the Hypotension Prediction IndexMay Be Overestimated Due to Selection Bias,”Anesthesiology, vol. 137, pp. 283–289,Sept. 2022. [3] A. Smith and Z. Turoczi, “Con: Hypotension Prediction Index—A New Tool to Pre-dict Hypotension in Cardiac Surgery?,”Journal of Cardiothoracic and Vascular Anes-thesia, vol. 37, pp. 2137–2140, Oct. 2023. [4] H.-C. Lee, Y. Park, S. B. Yoon, S. M. Yang, D. Park, and C.-W. Jung, “VitalDB,a high-fidelity multi-parameter vital signs database in surgical patients,”ScientificData, vol. 9, p. 279, June 2022.