Print Email Facebook Twitter Prediction of Post-induction Hypotension by Machine Learning Title Prediction of Post-induction Hypotension by Machine Learning Author Zhao, Shuoyan (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Dauwels, J.H.G. (mentor) Gürel, N.M. (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering | Signals and Systems Date 2023-08-23 Abstract Anesthesia-related hypotension is a significant concern during surgery, occurring shortly after induction and potentially leading to severe complications. Since the anesthetic drug is believed to have an important role in the occurrence of post-induction hypotension (PIH), anesthesiologists now advocate for the appropriate selection of anesthetics dosage to avoid PIH.To facilitate such decision-making, an accurate prediction of PIH associated with a certain dosage of anesthetics is necessary. This thesis presents a high-accuracy prediction model for PIH that supports anesthesia decision-making. The model is trained on data from the VitalDB database of 320 patients undergoing general anesthesia. The target output of this classification model is the occurrence of PIH, as defined through comprehensive analysis that incorporates clinical operations. Besides demographic data and vital signs, our model incorporates the dosage of propofol administered during the induction period as an input variable, mimicking real-world anesthetic plans. By employing the model in the target control infusion system of anesthesia, the anesthetics dosage can be varied as input, providing outcome predictions as security suggestions. An ensemble algorithm is employed to balance the prediction performance and the ability to elucidate the positive relationship between propofol and PIH risk, forming an anesthetics advice model. Compared to previous PIH prediction studies, our prediction model is validated in more reliable nested cross-validation approach and achieves a higher performance (precision of 0.83 and recall of 0.84). We believe utilizing demographic and dynamic vital signs to predict HIP can be useful in determining the appropriate anesthetic dosage plan, offering potential improvements in patient care and safety. Subject AnesthesiaMachine LearningHypotensionDosage Recommendation To reference this document use: http://resolver.tudelft.nl/uuid:94f28f3b-5a45-436f-a82f-a1944d008cb4 Part of collection Student theses Document type master thesis Rights © 2023 Shuoyan Zhao Files PDF SPS_MSc_thesis_S_Zhao_Aug15.pdf 6.7 MB Close viewer /islandora/object/uuid:94f28f3b-5a45-436f-a82f-a1944d008cb4/datastream/OBJ/view