Dimensionality reduction and ensemble of LSTMs for antimicrobial resistance prediction

Bacterial resistance to antibiotics has been rapidly increasing, resulting in a low antibiotic effectiveness even treating common infections. The presence of resistant pathogens in environments such as a hospital Intensive Care Unit (ICU) exacerbates the critical admission-acquired infections. This work focuses on the prediction of antibiotic resistance in Pseudomonas aeruginosa nosocomial infections at the ICU, using Long Short-Term Memory (LSTM) artificial neural networks as predictive method. The analyzed data were extracted from the Electronic Health Records (EHR) of patients admitted in the University Hospital of Fuenlabrada from 2004 to 2019, and were modeled as Multivariate Time Series. A data-driven dimensionality reduction method is built by adapting three feature importance techniques from the literature to the considered data, and proposing an algorithm for selecting the most appropriate number of features. This is done using LSTM sequential capabilities so that the temporal aspect of features is taken into account. Furthermore, an ensemble of LSTMs is used to reduce the performance variance. Our results indicate that the patient's admission information, the antibiotics administered during the ICU stay, and the previous antimicrobial resistance are the most important features. The proposed dimensionality reduction method dramatically reduces the number of features while considerably increasing the prediction performance. The variance in the performance is reduced by considering the ensemble of classifiers. In essence, the proposed framework achieve, in a computationally cost efficient manner, promising results for supporting decisions in this clinical task, characterized by high dimensionality, data scarcity and concept drift.This work has been partly supported by the Spanish Research Agency, grant numbers PID2019-106623RB-C41, AEI/10.13039/501100011033 (BigTheory), PID2019-107768RA-I00 (AAVis-BMR), by funding action by the Community of Madrid in the framework of the Multiannual Agreement with Rey Juan Carlos University in line of action 1 ‘‘Encouragement of Young Phd students investigation’’ Project Mapping-UCI (Ref F661), by the IDEAI-UPC Consolidated Research Group Grant from Catalan Agency of University and Research Grants (AGAUR, Generalitat de Catalunya) (2017 SGR 574) and by the Secretariat for Universities and Research of the Ministry of Research and Universities of the Government of Catalonia and the European Social Fund (2021 FI-B 00965).Peer ReviewedPostprint (published version

Evaluating the impact of climatic variability in wine production

No negócio de produção de vinhos a variabilidade do clima é uma das condicionantes mais importantes, sendo o aspeto mais crítico no que diz respeito ao processo de amadurecimento do fruto de maneira a que este possua as caracteristicas necessárias para a produção de um bom vinho. Acrescentando a esse fator, as variações climáticas têm conseqûencias nefastas não só para os trabalhadores dessa área como também nos terrenos utilizados para as vinhas. A realização de uma boa previsão e análise estatística das produções vinícolas anteriores podem ajudar empresas a poupar dinheiro e a preservar o ambiente. Assim, como possíveis soluções surgem diferentes alternativas para o processamento dos "datasets" de produção vinicola de anos passados e posterior análise, conseguindo assim identificar os diferentes componentes climáticos e os seus impactos na produção de vinhos. A solução será baseada na premissa de "Machine Learning" que consiste na construção de um modelo baseando-se nos dados já existentes fornecidos pelo "dataset"[3] , de forma a conseguir agrupar os dados de teste semelhantes em subgrupos de acordo com as suas características, para conseguir este resultado serão usadas Árvores de decisão, especificamente "Regression Trees" que consistem numa forma de fácil interpretação e compreensão do processo de agrupamento de subgrupos. Este processo evidenciaria a relação existente entre as séries meteorólogicas e o seu impacto na produção vinicola. A solução presente nesta dissertação é baseada na premissa de machine learning e visa inovar no sentido de aplicar o conceito de árvores de decisão a múltiplas séries temporais, tendo como objetivo identificar subgrupos de dados e agrupando-os em classesIn the wine production business climate variability is one of the most critical conditions, being essential in regards to the process of ripening fruits so that it possesses the required characteristics to produce a good wine. Adding to this factor, the climatic variations have disastrous consequences not only for wine producers and workers but also for the land used for vineyards. Performing a good forecasting and statistical analysis of the wineries previous productions can help businesses save money and preserve the environment. In regards to this problem, new solutions arise for the processing of "datasets" based on data from winery production of past years and with further analysis, it is possible to achieve and identify the different climatic components and their impact on wine production. The solution would be based on the premise of "Machine Learning" consisting in building a model based on existing data provided by the"Dataset" [3] in order to be able to group similar data into subgroups according to its characteristics, making use of Decision trees, more specifically Regression trees to perform the grouping. This process would demonstrate the relationship between the meteorological series and its impact on winery production. The solution presented is an easy to understand way to represent grouping of data. The main goal of this thesis is to establish the impact of the different climacteric conditions (temperature, precipitation) in wine production. The solution in this dissertation is based on the premise of machine learning and aims to innovate in order to apply the concept of decision trees to multiple time series, aiming to identify subgroups of data and grouping them into classes