Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2022The general growth and improved accessibility to electronic health records demands an identical level of
progress in terms of the research community regarding clinical models. The usage of machine learning
techniques is key to this development, and so they are increasingly being used in large medical databases
with the purpose of creating solutions that work for specified patients, no matter the task or the disease.
Acute kidney injury (AKI) is a broad disease defined by abrupt changes in renal function. AKI has
a high morbidity and mortality, with an increased focus on critically ill patients. The main goal of this
thesis is to study the development of AKI within a patient’s stay in the intensive care unit (ICU).
Data from the MIMIC-III database was used to collect information regarding the patients. After a
detailed exclusion criteria, those were evaluated in terms of AKI stages, with the purpose of predicting the
next value of AKI stage one hour after the sequence of information fed to the model. This can suggest the
capacity of the model at predicting the aggravation of a patient’s AKI condition. The sequences used have
hourly information for every feature, and were used sequences of 6h, 12h and 24h length. Self-attention
mechanisms were used to make the predictions, using an adaptation for multi-variate time series built
from the successfully used models on natural language processing (NLP) tasks.
The predictions on this work were made for two variations of the KDIGO classification system: one
where only the serum creatinine (SCr) criteria was taken into account to determine the patient’s AKI
stage, and other where both SCr and urine output (UO) were considered. While most works addressing
AKI only tend to use SCr values to determine the patient’s AKI condition, the results were compared
using both approaches and were better when using both SCr and UO. For those experiments, the model
achieved up to 68.05% accuracy predicting an episode of AKI, compared to the 66.67% accuracy achieved
using only SCr values, which outperformed state-of-the-art results for both cases.
Feature importance was also used for each dataset associated with the two variations of KDIGO
classification system to identify what were the most important features. Furthermore, final results were
compared when using all features versus only using the most 10 important ones
