4 research outputs found
Benchmarking machine learning models on multi-centre eICU critical care dataset
Progress of machine learning in critical care has been difficult to track, in
part due to absence of public benchmarks. Other fields of research (such as
computer vision and natural language processing) have established various
competitions and public benchmarks. Recent availability of large clinical
datasets has enabled the possibility of establishing public benchmarks. Taking
advantage of this opportunity, we propose a public benchmark suite to address
four areas of critical care, namely mortality prediction, estimation of length
of stay, patient phenotyping and risk of decompensation. We define each task
and compare the performance of both clinical models as well as baseline and
deep learning models using eICU critical care dataset of around 73,000
patients. This is the first public benchmark on a multi-centre critical care
dataset, comparing the performance of clinical gold standard with our
predictive model. We also investigate the impact of numerical variables as well
as handling of categorical variables on each of the defined tasks. The source
code, detailing our methods and experiments is publicly available such that
anyone can replicate our results and build upon our work.Comment: Source code to replicate the results
https://github.com/mostafaalishahi/eICU_Benchmar
Monitoring and detecting faults in wastewater treatment plants using deep learning
Wastewater treatment plants use many sensors to control energy consumption and discharge quality. These sensors produce a vast amount of data which can be efficiently monitored by automatic systems. Consequently, several different statistical and learning methods are proposed in the literature which can automatically detect faults. While these methods have shown promising results, the nonlinear dynamics and complex interactions of the variables in wastewater data necessitate more powerful methods with higher learning capacities. In response, this study focusses on modelling faults in the oxidation and nitrification process. Specifically, this study investigates a method based on deep neural networks (specifically, long short-term memory) compared with statistical and traditional machine-learning methods. The network is specifically designed to capture temporal behaviour of sensor data. The proposed method is evaluated on a real-life dataset containing over 5.1 million sensor data points. The method achieved a fault detection rate (recall) of over 92%, thus outperforming traditional methods and enabling timely detection of collective faults
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review
Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset