Jose Ramón Navarro-Cerdán

Type 2 diabetes (T2D) is becoming one of the leading health problems in Western societies, diminishing quality of life and consuming a significant share of healthcare resources. This study presents machine learning models for T2D diagnosis and prognosis, developed using heterogeneous data from a Spanish population dataset (Di@bet.es study). The models were trained exclusively on individuals classified as controls and undiagnosed diabetics, ensuring that the results are not influenced by treatment effects or behavioral changes due to disease awareness. Two data domains are considered: environmental (patient lifestyle questionnaires and measurements) and clinical (biochemical and anthropometric measurements). The preprocessing pipeline consists of four key steps: geospatial data extraction, feature engineering, missing data imputation, and quasi-constancy filtering. Two working scenarios (Environmental and Healthcare) are defined based on the features used, and applied to two targets (diagnosis and prognosis), resulting in four distinct models. The feature subsets that best predict the target have been identified based on permutation importance and sequential backward selection, reducing the number of features and, consequently, the cost of predictions. In the Environmental scenario, models achieved an AUROC of 0.86 for diagnosis and 0.82 for prognosis. The Healthcare scenario performed better, with an AUROC of 0.96 for diagnosis and 0.88 for prognosis. A partial dependence analysis of the most relevant features is also presented. An online demo page showcasing the Environmental and Healthcare T2D prognosis models is available upon request. Graphic

SEQENS is an ensemble method aimed at feature identification that has demonstrated strong performance in identifying relevant genes in high‐dimensional spaces, across different synthetic tasks. In this paper, we first introduce the differences between feature importance , feature selection (FS) and feature identification concepts. Following this, we present a framework based on SEQENS covering the following contributions: (1) computing the hypergeometric p‐ value of the features of a SEQENS output ranking in order to be able to establish a threshold between relevant and non‐relevant features; (2) extending SEQENS by introducing the use of preselected features as hypotheses of relevance in the sequential FS, which may help to attract other features that might exhibit weak correlation with the target on their own, but gain relevance when combined with the preselected ones and; (3) designing an automated process based on a 2D‐cascade of SEQENS ensembles to obtain a purged feature set , or PFS, that is, having as many relevant features, and as few non‐relevant, as possible. The framework presented, named pc–SEQENS, integrates the former techniques so that the PFS is used as a hypothesis of relevance in a SEQENS ensemble. Performance is analysed in a gene expression identification task using the E‐MTAB‐3732 public database and synthetic targets. pc–SEQENS is compared to other state‐of‐the‐art methods, including SEQENS to check the effect of using hypotheses of relevance. On average, the proposed framework identifies better the relevant genes, especially in unfavourable sample‐to‐dimension rates, and exhibits a stronger stability.

Data drift can significantly impact the outcome of a model. Early detection of data drift is crucial for ensuring user confidence in predictions. It allows the user to check if a particular model needs retraining using updated data to adapt to the evolving process dynamics. This study compares five different statistical tests, namely four unidimensional and a new multidimensional test (MSPC), to identify data drift in both mean and deviation. While some are designed to detect drift in mean only, like our multidimensional proposal, others respond to changes in both mean and deviation. However, our Hotelling multidimensional method can be trained once and then applied in a single stage to any data stream with several attributes, and it can identify the most relevant variables causing a data drift with one execution, thus avoiding the need for a single univariate test for each attribute. Moreover, our method yields the relative importance of each attribute for drift and allows users to increase or decrease the relative weight of each variable regarding drift detection. It also may be capable of detecting drift due to changes in multivariate interactions. This behavior is especially suitable for real-world scenarios, such as industry, finance, or healthcare environments.

Missing values are a major challenge in most data science projects working on real data. To avoid losing valuable information, imputation methods are used to fill in missing values with estimates, allowing the preservation of samples or variables that would otherwise be discarded. However, if the process is not well controlled, imputation can generate spurious values that introduce uncertainty and bias into the learning process. The abundance of univariate and multivariate imputation techniques, along with the complex trade-off between data reliability and preservation, makes it difficult to determine the best course of action to tackle missing values. In this work, we present ITI-IQA (Imputation Quality Assessment), a set of utilities designed to assess the reliability of various imputation methods, select the best imputer for any feature or group of features, and filter out features that do not meet quality criteria. Statistical tests are conducted to evaluate the suitability of every tested imputer, ensuring that no new biases are introduced during the imputation phase. The result is a trainable pipeline of filters and imputation methods that streamlines the process of dealing with missing data, supporting different data types: continuous, discrete, binary, and categorical. The toolbox also includes a suite of diagnosing methods and graphical tools to check measurements and results during and after handling missing data.

Publishers of Foundations and Trends, making research accessible

Publishers of Foundations and Trends, making research accessible

The objective was to assess risk of hospitalization and mortality of comorbidities using divisive hierarchical risk clustering to advice clinical interventions. Subjects and Methods: Data from the EHR of a general population, 3799885 adults, followed by 5 years. Model were performed using Spark and Scikit-learn and accuracy for the models was analyzed. Results: The number of models generated depends in part on the number of chronic diseases included (ex testing a sample of six diseases, a total number of 397 models for all-cause mortality and 431 models for hospitalization). The estimated models offered an ordered selection for the relevant clinical variables and their estimated risk as a group and for the individual patient in the group. Accuracy was assessed according to age, sex and the cardinality of the comorbid groups. A mobile version and dashboard were developed. Conclusion: The software developed stratified hospital admission and mortality risk in clusters of chronic diseases, and for a given patient, it could advise intensifying treatment or reallocating the patient risk.

This research aims to evaluate the Type 2 Diabetes (T2D) diagnosis and prognosis power from heterogeneous environmental, lifestyle and biochemistry data. Model estimation has previously addressed three main actions as: 1) Missingvalue imputation using specific univariant and multivariant imputers accommodated to each particular feature; 2) Quasi-constancy detection in variables; 3) Constructing geographical pollution and rent data from municipality information. Next, different T2D diagnosis and prognosis models are fitted and evaluated, showing increasing performance as more specific features become available while the prediction cost rises as a consequence of requiring more specific data. Finally, four models are obtained: two of them for T2D diagnosis and the other two for T2D prognosis respectively, with performances ranging from 73.3 to 95.41 AUC-ROC. One pair of diagnosis and prognosis models were thought for a global testing that can be done in general locations by only asking general lifestyle-related questions. On the other hand, the other pair, which achieves higher performances, is thought to be applied in a clinical environment where it is easy to obtain more specific biochemistry measures.