Abstract |
The continuous growth of high volumes of biomedical data in healthcare generates
significant challenges for their efficient management. This need has made inevitable the
adoption of big data infrastructures and relevant techniques from healthcare
organizations, in order for them to efficiently explore the wealth of real-world data
generated with the objective to improve the quality of healthcare services. In the
healthcare industry, various big data sources, that are characterized by heterogeneity,
exist. These include hospital information systems (HIS) and medical records of patients
(ΕHRs), results of laboratory procedures and examinations residing in relevant
information systems (Laboratory Information Systems - LIS), data from continuous patient
monitoring (e.g. in an Intensive Care Unit - ICU) and data from smart devices, such as
wearables. Also, very big data sets are generated from genomics-related clinical and
research work. Regarding genomics, the rate of growth over the last decade has also been
truly astonishing, with the total amount of sequence data produced doubling
approximately every seven months. This data requires efficient management and analysis
in order to derive meaningful and actionable information.
In developing such solutions, a range of challenges and complications associated with
each step of the pipeline for handling such healthcare big data sets need to be addressed.
These can only be resolved by using high-quality computing solutions for big data analysis.
Especially in the current situation of the COVID-19 pandemic, complications that might
occur after the onset of this disease are really important. An important such complication
is Acute Respiratory Distress Syndrome (ARDS), which is a serious respiratory condition
with high mortality and associated morbidity. A large number of basic and clinical studies
have demonstrated that early diagnosis and intervention are key to improving the survival
rate of patients with ARDS. Therefore, there is a pressing need for the development and
clinical testing of predictive models for ARDS events, which might improve the clinical
diagnosis or the management of ARDS.
In the present thesis, we focused on two distinct objectives; namely a) to design a
scalable data science platform, built on open source technologies, and b) to exploit the
platform and publically available big healthcare datasets to develop machine learning
models for predicting acute respiratory distress syndrome (ARDS) events through
commonly available parameters, including baseline characteristics and clinical and
laboratory parameters.
This thesis is divided into two main parts. The first part presents and analyzes in detail
all the procedures, materials, and methods adopted to develop this big data management
platform. We report on the complications and difficulties that arise in creating and using
such systems with large biomedical datasets, such as the MIMIC-III dataset. The second
part of the thesis describes how we exploit this clinical database, to perform an evaluation
study of our platform on a real world clinical scenario for ARDS. The objective of the study
was to develop and evaluate a novel application of machine learning models for
predicting acute respiratory distress syndrome (ARDS. We employ random forests and
logistic regression algorithmic models, trained on patient health record data for the early
prediction and diagnosis of ARDS. Our approach achieves better results in all metrics that
are based on AUC, when compared to relevant published efforts using the MIMIC III
dataset to develop predictive models of ARDS. Specifically, both of our algorithmic models
outperform in ARDS prediction, with 10-fold cross validated Random Forest being
dominant, according to AUC (95.1%), Accuracy (98.0%), Specificity (98.62%) and
Sensitivity (96.25%).
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