Predicting the Appearance of Hypotension during Hemodialysis Sessions Using Machine Learning Classifiers

Juan A. Gómez-Pulido, José M. Gómez-Pulido, Diego Rodríguez-Puyol, María-Luz Polo-Luque and Miguel Vargas-Lombardo
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Juan A. Gómez-Pulido: Department of Technologies of Computers and Communications, University of Extremadura, 10003 Cáceres, Spain
José M. Gómez-Pulido: Ramón y Cajal Institute for Health Research (IRYCIS), Department of Computer Science, University of Alcalá, 28801 Alcalá de Henares, Spain
Diego Rodríguez-Puyol: Department of Medicine and Medical Specialties, Research Foundation of the University Hospital Príncipe de Asturias, IRYCIS, Alcalá University, 28801 Alcalá de Henares, Madrid, Spain
María-Luz Polo-Luque: Department of Nursing and Physiotherapy, University of Alcalá, 28801 Alcalá de Henares, Spain
Miguel Vargas-Lombardo: E-Health and Supercomputing Research Group (GISES), Technological University of Panama, Panama City 0819-07289, Panama

IJERPH, 2021, vol. 18, issue 5, 1-17

Abstract: A patient suffering from advanced chronic renal disease undergoes several dialysis sessions on different dates. Several clinical parameters are monitored during the different hours of any of these sessions. These parameters, together with the information provided by other parameters of analytical nature, can be very useful to determine the probability that a patient may suffer from hypotension during the session, which should be specially watched since it represents a proven factor of possible mortality. However, the analytical information is not always available to the healthcare personnel, or it is far in time, so the clinical parameters monitored during the session become key to the prevention of hypotension. This article presents an investigation to predict the appearance of hypotension during a dialysis session, using predictive models trained from a large dialysis database, which contains the clinical information of 98,015 sessions corresponding to 758 patients. The prediction model takes into account up to 22 clinical parameters measured five times during the session, as well as the gender and age of the patient. This model was trained by means of machine learning classifiers, providing a success in the prediction higher than 80%.

Keywords: hemodialysis; hypotension; clinical monitoring; supervised learning; decision trees; support vector machines (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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