Year: 2006
Type: conference-paper
Abstract: Data mining is concerned with the automatic extraction of knowledge from data.Machine learning algorithms perform the data mining process and result in models that can be used for predictive purposes.Intensive care units are very data-rich environments where the vital functions of patients are continuously monitored.Data from different information sources is collected for each patient, which the physician uses to determine the condition of the patient and to administer the appropriate treatments and medications.Machine learning algorithms can be used to build predictive models based on the intensive care data.These models make use of all the available data to predict the future health-state of the patient, aiding the physician in his choice for better treatments.In this work we present the use of machine learning algorithms to develop predictive models for the application domain of intensive care medicine.Because of the particular characteristics of the data available in intensive care, we first introduce some challenging aspects about data mining in this domain.The main part of this dissertation is concerned with the application of machine learning algorithms to several prediction tasks that are performed by the medical staff in intensive care.We first present the use of standard machine learning techniques to predict survival, organ failure and inflammation in intensive care patients.The models learned are found to have similar predictive performance as the medical staff.We then develop a methodology for abstracting the dynamical information of the patients' vital signals and incorporate it in the data mining algorithms.This abstraction method, based on time-series analysis techniques, improves the performance of the predictive models when compared to those that use only static information.Finally, we present a data mining model that makes use of several information sources in intensive care, including the dynamical aspect of vital signals, in order to predict the moment of a patient's discharge from the intensive care unit.This prediction task is very relevant to physicians because it is indicative of the patient's condition, and to hospital administrators, because it allows for better planning of bed availability and for scheduling of surgical procedures.The data mining model developed results in better predictive performance than the resident medical staff in the intensive care unit.i And now that I find myself at the end of the journey that has been this Ph.D. I would like to express my gratitude to everyone that has helped me along the way, be it because we worked together, or simply because their presence made the journey that more pleasant.First of all I would like to thank my supervisor Hendrik, who presented me with the opportunity of working in the topic of this dissertation.For giving me the freedom to pursue my own research interests, and for always making time for me when I needed his advice on both research and non-research related issues.I am also very grateful to my supervisor Maurice for his silent support throughout the years.I would like to thank him specially for the time and dedication spent in reading and correcting the draft for this thesis.Since the beginning Jan Ramon has been of great help to me.At first providing me with guidance in unravelling the difficult and technical aspects of my studies, and later on as my official advisor hinting towards new and exciting possibilities for research.Thanks Jan.I would like to thank all the members of my jury for providing valuable insights and comments to the further improvement of this dissertation.Special thanks go to the foreign members of my jury, Nada Lavrac and Ameen Abu-Hanna, for making the trip to Leuven to be present at my preliminary defense, and also to Herman Neuckermans
Source: Lirias (KU Leuven)
Institution: KU Leuven
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7024008783