Persona: Gómez-Valades Batanero, Alba
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Publicación Ontologies for early detection of the Alzheimer Disease and other Neurodegenerative Diseases(Springer, 2019) Gómez-Valades Batanero, Alba; Martínez Tomás, Rafael; Rincón Zamorano, MarianoNowadays technologies allow an exponential generation of biomedical data, which must be indexed according to some standard criteria to be useful to the scientific and medical community, being neurology one of the areas in which the standardization is more necessary. Ontologies have been highlighted as one of the best options, with their capability of homogenise information, allowing their integration with other kind of information, and the inference of new information based on the data that is stored. We analyse and compare the approaches taken by different research groups inside the area of the Alzheimer’s disease, and the ontologies they developed with the objective of providing a common framework to standardize information, data recovery or as a part of an expert system. However, to make this approach work the ontologies must be maintained over the time, a critical point which is not been followed by any of the ontologies reviewed.Publicación Integrative Base Ontology for the Research Analysis of Alzheimer’s Disease-Related Mild Cognitive Impairment(Frontiers, 2021-02-04) Gómez-Valades Batanero, Alba; Martínez Tomás, Rafael; Rincón Zamorano, MarianoEarly detection of mild cognitive impairment (MCI) has become a priority in Alzheimer’s disease (AD) research, as it is a transitional phase between normal aging and dementia. However, information on MCI and AD is scattered across different formats and standards generated by different technologies, making it difficult to work with them manually. Ontologies have emerged as a solution to this problem due to their capacity for homogenization and consensus in the representation and reuse of data. In this context, an ontology that integrates the four main domains of neurodegenerative diseases, diagnostic tests, cognitive functions, and brain areas will be of great use in research. Here, we introduce the first approach to this ontology, the Neurocognitive Integrated Ontology (NIO), which integrates the knowledge regarding neuropsychological tests (NT), AD, cognitive functions, and brain areas. This ontology enables interoperability and facilitates access to data by integrating dispersed knowledge across different disciplines, rendering it useful for other research groups. To ensure the stability and reusability of NIO, the ontology was developed following the ontology-building life cycle, integrating and expanding terms from four different reference ontologies. The usefulness of this ontology was validated through use-case scenarios.Publicación Designing an effective semantic fluency test for early MCI diagnosis with machine learning(Elsevier, 2024-08-16) Gómez-Valades Batanero, Alba; Martínez Tomás, Rafael; Rincón Zamorano, MarianoSemantic fluency tests are one of the key tests used in batteries for the early detection of Mild Cognitive Impairment (MCI) as the impairment in speech and semantic memory are among the first symptoms, attracting the attention of a large number of studies. Several new semantic categories and variables capable of providing complementary information of clinical interest have been proposed to increase their effectiveness. However, this also extends the time required to complete all tests and get the overall diagnosis. Therefore, there is a need to reduce the number of tests in the batteries and thus the time spent on them while maintaining or increasing their effectiveness. This study used machine learning methods to determine the smallest and most efficient combination of semantic categories and variables to achieve this goal. We utilized a database containing 423 assessments from 141 subjects, with each subject having undergone three assessments spaced approximately one year apart. Subjects were categorized into three diagnostic groups: Healthy (if diagnosed as healthy in all three assessments), stable MCI (consistently diagnosed as MCI), and heterogeneous MCI (when exhibiting alternations between healthy and MCI diagnoses across assessments). We obtained that the most efficient combination to distinguish between these categories of semantic fluency tests included the animals and clothes semantic categories with the variables corrects, switching, clustering, and total clusters. This combination is ideal for scenarios that require a balance between time efficiency and diagnosis capability, such as population-based screenings.Publicación Early detection of mild cognitive impairment through neuropsychological tests in population screenings: a decision support system integrating ontologies and machine learning(Frontiers Media, 2024-10-16) Gómez-Valades Batanero, Alba; Martínez Tomás, Rafael; García Herranz, Sara; Bjørnerud, Atle; Rincón Zamorano, MarianoMachine learning (ML) methodologies for detecting Mild Cognitive Impairment (MCI) are progressively gaining prevalence to manage the vast volume of processed information. Nevertheless, the black-box nature of ML algorithms and the heterogeneity within the data may result in varied interpretations across distinct studies. To avoid this, in this proposal, we present the design of a decision support system that integrates a machine learning model represented using the Semantic Web Rule Language (SWRL) in an ontology with specialized knowledge in neuropsychological tests, the NIO ontology. The system’s ability to detect MCI subjects was evaluated on a database of 520 neuropsychological assessments conducted in Spanish and compared with other well-established ML methods. Using the F2 coefficient to minimize false negatives, results indicate that the system performs similarly to other well-established ML methods (F2TE2=0.830, only below bagging, F2BAG=0.832) while exhibiting other significant attributes such as explanation capability and data standardization to a common framework thanks to the ontological part. On the other hand, the system’s versatility and ease of use were demonstrated with three additional use cases: evaluation of new cases even if the acquisition stage is incomplete (the case records have missing values), incorporation of a new database into the integrated system, and use of the ontology capabilities to relate different domains. This makes it a useful tool to support physicians and neuropsychologists in population-based screenings for early detection of MCI.