Hidalgo González, José Ignacio

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First Name
José Ignacio
Last Name
Hidalgo González
Universidad Complutense de Madrid
Faculty / Institute
Filología Clásica
Filología Latina
UCM identifierDialnet ID

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Now showing 1 - 7 of 7
  • Publication
    Diagnosing hospital bacteraemia in the framework of predictive, preventive and personalised medicine using electronic health records and machine learning classifiers
    (Springer Nature, 2021-08-31) Garnica Alcázar, Oscar; Gómez, Diego; Ramos, Víctor; Hidalgo González, José Ignacio; Ruiz Giardín, José Manuel
    Background The bacteraemia prediction is relevant because sepsis is one of the most important causes of morbidity and mortality. Bacteraemia prognosis primarily depends on a rapid diagnosis. The bacteraemia prediction would shorten up to 6 days the diagnosis, and, in conjunction with individual patient variables, should be considered to start the early administration of personalised antibiotic treatment and medical services, the election of specific diagnostic techniques and the determination of additional treatments, such as surgery, that would prevent subsequent complications. Machine learning techniques could help physicians make these informed decisions by predicting bacteraemia using the data already available in electronic hospital records. Objective This study presents the application of machine learning techniques to these records to predict the blood culture’s outcome, which would reduce the lag in starting a personalised antibiotic treatment and the medical costs associated with erroneous treatments due to conservative assumptions about blood culture outcomes. Methods Six supervised classifiers were created using three machine learning techniques, Support Vector Machine, Random Forest and K-Nearest Neighbours, on the electronic health records of hospital patients. The best approach to handle missing data was chosen and, for each machine learning technique, two classification models were created: the first uses the features known at the time of blood extraction, whereas the second uses four extra features revealed during the blood culture. Results The six classifiers were trained and tested using a dataset of 4357 patients with 117 features per patient. The models obtain predictions that, for the best case, are up to a state-of-the-art accuracy of 85.9%, a sensitivity of 87.4% and an AUC of 0.93. Conclusions Our results provide cutting-edge metrics of interest in predictive medical models with values that exceed the medical practice threshold and previous results in the literature using classical modelling techniques in specific types of bacteraemia. Additionally, the consistency of results is reasserted because the three classifiers’ importance ranking shows similar features that coincide with those that physicians use in their manual heuristics. Therefore, the efficacy of these machine learning techniques confirms their viability to assist in the aims of predictive and personalised medicine once the disease presents bacteraemia-compatible symptoms and to assist in improving the healthcare economy.
  • Publication
    Aplicaciones didácticas del diccionario de colocaciones latinas (DiCoLat) en la red
    (2022-06-30) Baños Baños, José Miguel; Hidalgo González, José Ignacio; Jiménez Martínez, María Isabel; López Martín, Iván; Salas Jiménez, Guillermo; Tarriño Ruiz, Eusebia; Tur Altarriba, Cristina
    En la memoria se recogen los objetivos propuestos en la presentación del proyecto (§ 1), los objetivos alcanzados (§ 2), la metodología empleada en el proyecto (§ 3), los recursos humanos (§ 4) y desarrollo de actividades (5) y los anexos que completan esta memoria (§ 6): una versión provisional de la «Guía práctica de la aplicación del usuario» (Anexo 1), que explica la arquitectura y características del servidor interno de DiCoLat, y la ilustración de algunas de las posibilidades de búsquedas en el servidor externo (Anexo 2).
  • Publication
    La Base de Datos COLAT (II) y su aplicación docente: colocaciones y construcciones con verbo soporte en latín (memoria del proyecto)
    (2020-09-29) Baños Baños, José Miguel; Alonso Fernández, Zoa; Ayora Estevan, Daniel; Hidalgo González, José Ignacio; Jiménez López, María Dolores; Jiménez Martínez, María Isabel; López Martín, Ivan; Mendozar Cruz, Juan; Rodríguez Martín, Elvira; Salas Jiménez, Guillermo; Tarriño Ruiz, Eusebia; Tierno Casado, Clara María; Tur Altarriba, Cristina
  • Publication
    Un diccionario de colocaciones en latín (I): su aplicación a la traducción de textos y a la enseñanza de la gramática.
    (2017-06-29) Baños Baños, José Miguel; Tur Altarriba, Cristina; López Martín, Iván; Tierno Casado, Clara María; Hidalgo González, José Ignacio; Mendozar Cruz, Juan; Jiménez Martínez, María Isabel
  • Publication
    Evaluating the Influence of Mood and Stress on Glycemic Variability in People with T1DM Using Glucose Monitoring Sensors and Pools
    (MPDI, 2022-04-11) Velasco Cabo, José Manuel; Botella Serrano, Marta; Sánchez Sánchez, Almudena; Aramendi, Aranzazu; Martínez, Remedios; Maqueda, Esther; Garnica Alcázar, Oscar; Contador, Sergio; Lanchares Dávila, Juan; Hidalgo González, José Ignacio
    Objective: Assess in a sample of people with type 1 diabetes mellitus whether mood and stress influence blood glucose levels and variability. Material and Methods: Continuous glucose monitoring was performed on 10 patients with type 1 diabetes mellitus, where interstitial glucose values were recorded every 15 min. A daily survey was conducted through Google Forms, collecting information on mood and stress. The day was divided into six slots of 4-h each, asking the patient to assess each slot in relation to mood (sad, normal or happy) and stress (calm, normal or nervous). Different measures of glycemic control (arithmetic mean and percentage of time below/above the target range) and variability (standard deviation, percentage coefficient of variation, mean amplitude of glycemic excursions and mean of daily differences) were calculated to relate the mood and stress perceived by patients with blood glucose levels and glycemic variability. A hypothesis test was carried out to quantitatively compare the data groups of the different measures using the Student’s t-test. Results: Statistically significant differences (p-value < 0.05) were found between different levels of stress. In general, average glucose and variability decrease when the patient is calm. There are statistically significant differences (p-value < 0.05) between different levels of mood. Variability increases when the mood changes from sad to happy. However, the patient’s average glucose decreases as the mood improves. Conclusions: Variations in mood and stress significantly influence blood glucose levels, and glycemic variability in the patients analyzed with type 1 diabetes mellitus. Therefore, they are factors to consider for improving glycemic control. The mean of daily differences does not seem to be a good indicator for variability.
  • Publication
    La base de datos COLAT (I): las colocaciones verbo-nominales en la docencia de la lengua latina
    (2019-05-30) Baños Baños, José Miguel; Alonso Fernández, Zoa; Jiménez Martínez, María Isabel; Tur Altarriba, Cristina; Mendozar Cruz, Juan; López Martín, Iván; Ayora Estevan, Daniel; Tierno Casado, Clara María; Hidalgo González, José Ignacio; Rodríguez Martín, Elvira; Boned Fernández, María Belén
  • Publication
    Un diccionario de colocaciones en latín (II): su aplicación a la traducción de textos.
    (2018-07-05) Baños Baños, José Miguel; Alonso Fernández, Zoa; Ayora Estevan, Daniel; Hidalgo González, José Ignacio; Jiménez Martínez, María Isabel; López Martín, Iván; Mendózar Cruz, Juan; Rodríguez Martín, Elvira; Tierno Casado, Clara María; Tur Altarriba, Cristina