Examinando por Autor "Mora-Jiménez, Inmaculada"

Mostrando 1 - 8 de 8

A streaming data visualization framework for supporting decision-making in the Intensive Care Unit
(Elsevier, 2023) Mohedano-Munoz, Miguel A.; Soguero-Ruiz, Cristina; Mora-Jiménez, Inmaculada; Rubio-Sánchez, Manuel; Álvarez-Rodríguez, Joaquín; Sanchez, Alberto
The number of reporting activities in real time has increased over the last years. This situation has pushed the need for providing real time analysis and visualizations to support decision-making. We propose a visualization framework for exploratory data analysis of multivariate data streams that relies on dimensionality reduction and machine learning techniques for plotting the data in two dimensions. Users can demarcate regions of interest for their study, and use them to make predictions or to decide when to train a new model. The knowledge gained from these visualizations allows users to: (i) characterize the data stream scenario; (ii) track the evolution of a case of interest; and (iii) configure and raise alarms according to the user-defined regions. We illustrate the effectiveness of our proposal through a case study analyzing real-world streaming data to identify patients with multi-drug resistant bacteria when they are in a hospital intensive care unit. Our visualization framework enables the patient follow-up which can allow clinicians to support decisions about the health status evolution of a particular patient. This could provide information for deciding on a particular treatment or whether to isolate patients with a high risk of having multi-drug resistant bacteria since their presence boosts infections in intensive care units.
Dimensionality reduction and ensemble of LSTMs for antimicrobial resistance prediction
(Elsevier, 2023-02-14) Hernàndez-Carnerero, Àlvar; Sànchez-Marrè, Miquel; Mora-Jiménez, Inmaculada; Soguero-Ruiz, Cristina; Martínez-Agüero, Sergio; Álvarez-Rodríguez, Joaquín
Bacterial resistance to antibiotics has been rapidly increasing, resulting in 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 the predictive method. The analyzed data were extracted from the Electronic Health Records (EHR) of patients admitted to 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 variance in performance. 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 risk factors. Compared to other conventional dimensionality reduction schemes, our approach is able to improve performance while reducing the number of features for most of the experiments. 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.
dtwParallel: A Python package to efficiently compute dynamic time warping between time series
(Elsevier, 2023) Escudero-Arnanz, Óscar; G. Marques, Antonio; Soguero-Ruiz, Cristina; Mora-Jiménez, Inmaculada; Robles, Gregorio
dtwParallel is a Python package that computes the Dynamic Time Warping (DTW) distance between a collection of (multivariate) time series (MTS). dtwParallel incorporates the main functionalities available in current DTW libraries and novel functionalities such as parallelization, computation of similarity (kernel-based) values, and consideration of data with different types of features (categorical, real-valued, . . . ). A low-floor, high-ceiling, and wide-walls software design principle has been adopted, envisioning uses in education, research, and industry. The source code and documentation of the package are available at https://github.com/oscarescuderoarnanz/dtwParallel.
Interpretable clinical time-series modeling with intelligent feature selection for early prediction of antimicrobial multidrug resistance
(Elsevier, 2022) Martínez-Agüero, Sergio; Soguero-Ruiz, Cristina; Alonso-Moral, Jose M.; Mora-Jiménez, Inmaculada; Álvarez-Rodríguez, Joaquín; Marques, Antonio G.
Electronic health records provide rich, heterogeneous data about the evolution of the patients’ health status. However, such data need to be processed carefully, with the aim of extracting meaningful information for clinical decision support. In this paper, we leverage interpretable (deep) learning and signal processing tools to deal with multivariate time-series data collected from the Intensive Care Unit (ICU) of the University Hospital of Fuenlabrada (Madrid, Spain). The presence of antimicrobial multidrug-resistant (AMR) bacteria is one of the greatest threats to the health system in general and to the ICUs in particular due to the critical health status of the patients therein. Thus, early identification of bacteria at the ICU and early prediction of their antibiotic resistance are key for the patients’ prognosis. While intelligent data-based processing and learning schemes can contribute to this early prediction, their acceptance and deployment in the ICUs require the automatic schemes to be not only accurate but also understandable by clinicians. Accordingly, we have designed trustworthy intelligent models for the early prediction of AMR based on the combination of meaningful feature selection with interpretable recurrent neural networks. These models were created using irregularly sampled clinical measurements, both considering the health status of the patient and the global ICU environment. We explored several strategies to cope with strongly imbalance data, since only a few ICU patients are infected by AMR bacteria. It is worth noting that our approach exhibits a good balance between performance and interpretability, especially when considering the difficulty of the classification task at hand. A multitude of factors are involved in the emergence of AMR (several of them not fully understood), and the records only contain a subset of them. In addition, the limited number of patients, the imbalance between classes, and the irregularity of the data render the problem harder to solve. Our models are also enriched with SHAP post-hoc interpretability and validated by clinicians who considered model understandability and trustworthiness of paramount concern for pragmatic purposes. Moreover, we use linguistic fuzzy systems to provide clinicians with explanations in natural language. Such explanations are automatically generated from a pool of interpretable rules that describe the interaction among the most relevant features identified by SHAP. Notice that clinicians were especially satisfied with new insights provided by our models. Such insights helped them to trust the automatic schemes and use them to make (better) decisions to mitigate AMR spreading in the ICU. All in all, this work paves the way towards more comprehensible time-series analysis in the context of early AMR prediction in ICUs and reduces the time of detection of infectious diseases, opening the door to better hospital care.
Interpreting clinical latent representations using autoencoders and probabilistic models
(Elsevier, 2021) Chushig-Muzo, David; Soguero-Ruiz, Cristina; Bohoyo, Pablo de Miguel; Mora-Jiménez, Inmaculada
Electronic health records (EHRs) are a valuable data source that, in conjunction with deep learning (DL) methods, have provided important outcomes in different domains, contributing to supporting decision-making. Owing to the remarkable advancements achieved by DL-based models, autoencoders (AE) are becoming extensively used in health care. Nevertheless, AE-based models are based on nonlinear transformations, resulting in black-box models leading to a lack of interpretability, which is vital in the clinical setting. To obtain insights from AE latent representations, we propose a methodology by combining probabilistic models based on Gaussian mixture models and hierarchical clustering supported by Kullback-Leibler divergence. To validate the methodology from a clinical viewpoint, we used real-world data extracted from EHRs of the University Hospital of Fuenlabrada (Spain). Records were associated with healthy and chronic hypertensive and diabetic patients. Experimental outcomes showed that our approach can find groups of patients with similar health conditions by identifying patterns associated with diagnosis and drug codes. This work opens up promising opportunities for interpreting representations obtained by the AE-based model, bringing some light to the decision-making process made by clinical experts in daily practice.
Learning and visualizing chronic latent representations using electronic health records
(BMC, 2022-09-05) Chushig-Muzo, David; Soguero-Ruiz, Cristina; Miguel Bohoyo, Pablo; Mora-Jiménez, Inmaculada
Background: Nowadays, patients with chronic diseases such as diabetes and hypertension have reached alarming numbers worldwide. These diseases increase the risk of developing acute complications and involve a substantial economic burden and demand for health resources. The widespread adoption of Electronic Health Records (EHRs) is opening great opportunities for supporting decision-making. Nevertheless, data extracted from EHRs are complex (heterogeneous, high-dimensional and usually noisy), hampering the knowledge extraction with conventional approaches. Methods: We propose the use of the Denoising Autoencoder (DAE), a Machine Learning (ML) technique allowing to transform high-dimensional data into latent representations (LRs), thus addressing the main challenges with clinical data. We explore in this work how the combination of LRs with a visualization method can be used to map the patient data in a two-dimensional space, gaining knowledge about the distribution of patients with diferent chronic conditions. Furthermore, this representation can be also used to characterize the patient’s health status evolution, which is of paramount importance in the clinical setting. Results: To obtain clinical LRs, we considered real-world data extracted from EHRs linked to the University Hospital of Fuenlabrada in Spain. Experimental results showed the great potential of DAEs to identify patients with clinical patterns linked to hyper‑ tension, diabetes and multimorbidity. The procedure allowed us to fnd patients with the same main chronic disease but diferent clinical characteristics. Thus, we identifed two kinds of diabetic patients with diferences in their drug therapy (insulin and non-insulin dependant), and also a group of women afected by hypertension and gestational diabetes. We also present a proof of concept for mapping the health status evolution of synthetic patients when considering the most signifcant diagnoses and drugs associated with chronic patients. Conclusion: Our results highlighted the value of ML techniques to extract clinical knowledge, supporting the identifcation of patients with certain chronic conditions. Furthermore, the patient’s health status progression on the two-dimensional space might be used as a tool for clinicians aiming to characterize health conditions and identify their more relevant clinical codes.
Scaled Radial Axes for Interactive Visual Feature Selection: A Case Study for Analyzing Chronic Conditions
(2018-06-15) Sanchez, Alberto; Soguero-Ruiz, Cristina; Mora-Jiménez, Inmaculada; Rivas-Flores, Francisco Javier; Lehmann, Dirk Joachim; Rubio-Sánchez, Manuel
In statistics, machine learning, and related fields, feature selection is the process of choosing a smaller subset of features to work with. This is an important topic since selecting a subset of features can help analysts to interpret models and data, and to decrease computational runtimes. While many techniques are purely automatic, the data visualization community has produced a number of interactive approaches where users can make decisions taking into account their domain knowledge. In this paper we propose a new visualization technique based on radial axes that allows analysts to perform feature selection effectively, in contrast to previous radial axes methods. This is achieved by employing alternative scaled axes that provide insight regarding the features that have a smaller contribution to the visualizations. Therefore, analysts can use the technique to carry out interactive backwards feature elimination, by discarding the least relevant features according to the information on the plots and their expertise. Our approach can be coupled with any linear dimensionality reduction method, and can be used when performing analyses of cluster structure, correlations, class separability, etc. Specifically, in this paper we focus on combining the proposed technique with methods designed for classification. Lastly, we illustrate the effectiveness of our proposal through a case study analyzing high-dimensional medical chronic conditions data. In particular, clinicians have used the technique for determining the most important features that discriminate between patients with diabetes and high blood pressure.
Visually guided classification trees for analyzing chronic patients
(2020-03-11) Soguero-Ruiz, Cristina; Mora-Jiménez, Inmaculada; Mohedano-Munoz, Miguel Ángel; Rubio-Sánchez, Manuel; de Miguel-Bohoyo, Pablo; Sanchez, Alberto
Background: Chronic diseases are becoming more widespread each year in developed countries, mainly due to increasing life expectancy. Among them, diabetes mellitus (DM) and essential hypertension (EH) are two of the most prevalent ones. Furthermore, they can be the onset of other chronic conditions such as kidney or obstructive pulmonary diseases. The need to comprehend the factors related to such complex diseases motivates the development of interpretative and visual analysis methods, such as classification trees, which not only provide predictive models for diagnosing patients, but can also help to discover new clinical insights. Results: In this paper, we analyzed healthy and chronic (diabetic, hypertensive) patients associated with the University Hospital of Fuenlabrada in Spain. Each patient was classified into a single health status according to clinical risk groups (CRGs). The CRGs characterize a patient through features such as age, gender, diagnosis codes, and drug codes. Based on these features and the CRGs, we have designed classification trees to determine the most discriminative decision features among different health statuses. In particular, we propose to make use of statistical data visualizations to guide the selection of features in each node when constructing a tree. We created several classification trees to distinguish among patients with different health statuses. We analyzed their performance in terms of classification accuracy, and drew clinical conclusions regarding the decision features considered in each tree. As expected, healthy patients and patients with a single chronic condition were better classified than patients with comorbidities. The constructed classification trees also show that the use of antipsychotics and the diagnosis of chronic airway obstruction are relevant for classifying patients with more than one chronic condition, in conjunction with the usual DM and/or EH diagnoses. Conclusions: We propose a methodology for constructing classification trees in a visually guided manner. The approach allows clinicians to progressively select the decision features at each of the tree nodes. The process is guided by exploratory data analysis visualizations, which may provide new insights and unexpected clinical information.