Investigating prognostic factors in sepsis using computational intelligence methods
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Inclou dades d'ús des de 2022
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hdl:2117/97503
Tipus de documentProjecte Final de Màster Oficial
Data2016-10
Condicions d'accésAccés obert
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Abstract
Sepsis is a major challenge in medicine. It is in fact a traversal condition affecting people of all
ages and is not respectful of lifestyle choices. There are many diseases in medicine whose definition
is uncertain and cause high rates of mortality in medical services, and Sepsis is a flagship example.
In an intensive care unit (ICU), patients in an advanced stage of Sepsis carries a high burden,
namely a high mortality rate (about 50%) and higher costs of treatment compared with other ICU
patients.
After an operation, patients have a tendency to develop a phenomenon related to the mechanism
of immune system. The pathophysiology of Sepsis in humans is poorly understood, even is one
of the main causes of death for non-coronary ICU patients. It is a traversal condition affecting
people of all ages. This syndrome was defined by consensus statement in 1992 to consist of certain
criteria. Severe sepsis was defined as organ failure in the setting of sepsis, and septic shock was
defined as severe sepsis where the organ failure was hypotension.
The ICU environment is one of the scenarios in which critical decision making tasks are most
relevant with respect to the outcome for the patient, and it is in this specific context that this
thesis tries to make a contribution.
The research reported in this document deals with the problem of Sepsis data analysis in general.
On the one hand, a causal relationship study is made over a set of roughly one hundred
different ICU measurements to detect hidden patterns among them, and to find the direct conditioners
of the patients outcome, by means of probabilistic approaches (i.e. Bayesian Networks).
On the other hand, the problem of survival prediction in patients that have suffered Septic
Shock is faced applying Computational Intelligence (i.e. Artificial Neural Networks) and Machine
learning approaches (Support Vector Machines), and more specifically in two different approaches:
(1) predicting directly the outcome of the patient and (2) predicting the organ dysfunction risk,
that can also lead patient to death or severe cognitive consequences.
The data set used in this work is the public available MEDAN database, which consists of 412
patients that have suffered abdominal Septic Shock, of whom 201 died. The data were recorded
from 71 German Intensive Care Units from 1998 to 2002.
TitulacióMÀSTER UNIVERSITARI EN INTEL·LIGÈNCIA ARTIFICIAL (Pla 2012)
Col·leccions
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120109.pdf | 3,309Mb | Visualitza/Obre |