English
norskBlar i forfatter "Soguero-Ruiz, Cristina"
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Analysis of free text in electronic health records for identification of cancer patient trajectories
(Journal article; Tidsskriftartikkel; Peer reviewed, 2017-04-07)With an aging patient population and increasing complexity in patient disease trajectories, physicians are often met with complex patient histories from which clinical decisions must be made. Due to the increasing rate of adverse events and hospitals facing financial penalties for readmission, there has never been a greater need to enforce evidence-led medical decision-making using available ... -
Clinical Synthetic Data Generation to Predict and Identify Risk Factors for Cardiovascular Diseases
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-01-21)Noncommunicable diseases are among the most significant health threats in our society, being cardiovascular diseases (CVD) the most prevalent. Because of the severity and prevalence of these illnesses, early detection and prevention are critical for reducing the worldwide health and economic burden. Though machine learning (ML) methods usually outperform conventional approaches in many domains, class ... -
Evaluation of Synthetic Categorical Data Generation Techniques for Predicting Cardiovascular Diseases and Post-Hoc Interpretability of the Risk Factors
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-03-23)Machine Learning (ML) methods have become important for enhancing the performance of decision-support predictive models. However, class imbalance is one of the main challenges for developing ML models, because it may bias the learning process and the model generalization ability. In this paper, we consider oversampling methods for generating synthetic categorical clinical data aiming to improve the ... -
Learning similarities between irregularly sampled short multivariate time series from EHRs
(Conference object; Konferansebidrag, 2016-12-04)A large fraction of the Electronic Health Records consists of clinical multivariate time series. Building models for extracting information from these is important for improving the understanding of diseases, patient care and treatment. Such time series are oftentimes particularly challenging since they are characterized by multiple, possibly dependent variables, length variability and irregular ... -
Maximizing Interpretability and Cost-Effectiveness of Surgical Site Infection (SSI) Predictive Models Using Feature-Specific Regularized Logistic Regression on Preoperative Temporal Data
(Journal article; Tidsskriftartikkel; Peer reviewed, 2019-02-19)This study describes a novel approach to solve the surgical site infection (SSI) classification problem. Feature engineering has traditionally been one of the most important steps in solving complex classification problems, especially in cases with temporal data. The described novel approach is based on abstraction of temporal data recorded in three temporal windows. Maximum likelihood L1-norm ... -
Noisy multi-label semi-supervised dimensionality reduction
(Journal article; Tidsskriftartikkel; Peer reviewed, 2019-01-29)Noisy labeled data represent a rich source of information that often are easily accessible and cheap to obtain, but label noise might also have many negative consequences if not accounted for. How to fully utilize noisy labels has been studied extensively within the framework of standard supervised machine learning over a period of several decades. However, very little research has been conducted ... -
On the Use of Time Series Kernel and Dimensionality Reduction to Identify the Acquisition of Antimicrobial Multidrug Resistance in the Intensive Care Unit
(Conference object; Konferansebidrag, 2021)The acquisition of Antimicrobial Multidrug Resistance (AMR) in patients admitted to the Intensive Care Units (ICU) is a major global concern. This study analyses data in the form of multivariate time series (MTS) from 3476 patients recorded at the ICU of University Hospital of Fuenlabrada (Madrid) from 2004 to 2020. 18% of the patients acquired AMR during their stay in the ICU. The goal of ... -
Time series cluster kernel for learning similarities between multivariate time series with missing data
(Journal article; Tidsskriftartikkel; Peer reviewed, 2017-12-06)<p>Similarity-based approaches represent a promising direction for time series analysis. However, many such methods rely on parameter tuning, and some have shortcomings if the time series are multivariate (MTS), due to dependencies between attributes, or the time series contain missing data. In this paper, we address these challenges within the powerful context of kernel methods by proposing the ... -
Using anchors from free text in electronic health records to diagnose postoperative delirium
(Journal article; Tidsskriftartikkel; Peer reviewed, 2017-09-19)Objectives:<br> Postoperative delirium is a common complication after major surgery among the elderly. Despite its potentially serious consequences, the complication often goes undetected and undiagnosed. In order to provide diagnosis support one could potentially exploit the information hidden in free text documents from electronic health records usin ...
