Training of Machine Learning Models for Recurrence Prediction in Patients with Respiratory Pathologies

Proceeding paper[Abstract] Information extracted from electronic health records (EHRs) is used for predictive tasks and clinical pattern recognition. Machine learning techniques also allow the extraction of knowledge from EHR. This study is a continuation of previous work in which EHRs were exploited to make predictions about patients with respiratory diseases. In this study, we will try to predict the recurrence of patients with respiratory diseases using four different machine learning algorithms.Centro de Investigación de Galicia CITIC and Campus Innova (agreement I+D+ 2019-20) is funded by Consellería de Educación, Universidade e Formación Profesional from Xunta de Galicia and European Union (European Regional Development Fund - FEDER Galicia 2014-2020 Program) by grant ED431G 2019/01 and Universidade da Coruña. Partially supported by the Spanish Ministry of Science (Challenges of Society 2019) PID2019-104323RB-C33Xunta de Galicia; ED431G 2019/0

Electronic Health Records Exploitation Using Artificial Intelligence Techniques

[Abstract] The exploitation of electronic health records (EHRs) has multiple utilities, from predictive tasks and clinical decision support to pattern recognition. Artificial Intelligence (AI) allows to extract knowledge from EHR data in a practical way. In this study, we aim to construct a Machine Learning model from EHR data to make predictions about patients. Specifically, we will focus our analysis on patients suffering from respiratory problems. Then, we will try to predict whether those patients will have a relapse in less than 6, 12 or 18 months. The main objective is to identify the characteristics that seem to increase the relapse risk. At the same time, we propose an exploratory analysis in search of hidden patterns among data. These patterns will help us to classify patients according to their specific conditions for some clinical variables.Centro de Investigación de Galicia CITIC is funded by Consellería de Educación, Universidades e Formación Profesional from Xunta de Galicia and European Union (European Regional Development Fund—FEDER Galicia 2014-2020 Program) by grant ED431G 2019/01. Partially supported by the Spanish Ministry of Science (Challenges of Society 2019) PID2019-104323RB-C33Xunta de Galicia; ED431G 2019/0

Random Forest-Based Prediction of Stroke Outcome

[Abstract] We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3-months after admission. The dataset consisted of patients with ischemic stroke (IS) and non-traumatic intracerebral hemorrhage (ICH) admitted to Stroke Unit of a European Tertiary Hospital prospectively registered. We identified the main variables for machine learning Random Forest (RF), generating a predictive model that can estimate patient mortality/morbidity according to the following groups: (1) IS + ICH, (2) IS, and (3) ICH. A total of 6022 patients were included: 4922 (mean age 71.9 ± 13.8 years) with IS and 1100 (mean age 73.3 ± 13.1 years) with ICH. NIHSS at 24, 48 h and axillary temperature at admission were the most important variables to consider for evolution of patients at 3-months. IS + ICH group was the most stable for mortality prediction [0.904 ± 0.025 of area under the receiver operating characteristics curve (AUC)]. IS group presented similar results, although variability between experiments was slightly higher (0.909 ± 0.032 of AUC). ICH group was the one in which RF had more problems to make adequate predictions (0.9837 vs. 0.7104 of AUC). There were no major differences between IS and IS + ICH groups according to morbidity prediction (0.738 and 0.755 of AUC) but, after checking normality with a Shapiro Wilk test with the null hypothesis that the data follow a normal distribution, it was rejected with W = 0.93546 (p-value < 2.2e−16). Conditions required for a parametric test do not hold, and we performed a paired Wilcoxon Test assuming the null hypothesis that all the groups have the same performance. The null hypothesis was rejected with a value < 2.2e−16, so there are statistical differences between IS and ICH groups. In conclusion, machine learning algorithms RF can be effectively used in stroke patients for long-term outcome prediction of mortality and morbidity.This study was partially supported by grants from the Spanish Ministry of Science and Innovation (SAF2017-84267-R), Xunta de Galicia (Axencia Galega de Innovación (GAIN): IN607A2018/3), Instituto de Salud Carlos III (ISCIII) (PI17/00540, PI17/01103), Spanish Research Network on Cerebrovascular Diseases RETICS-INVICTUS PLUS (RD16/0019) and by the European Union FEDER program. T. Sobrino (CPII17/00027), F. Campos (CPII19/00020) are recipients of research contracts from the Miguel Servet Program (Instituto de Salud Carlos III). General Directorate of Culture, Education and University Management of Xunta de Galicia (ED431G/01,252 ED431D 2017/16), “Galician Network for Colorectal Cancer Research" (Ref. ED431D 2017/23), Competitive Reference Groups (ED431C 2018/49), Spanish Ministry of Economy and Competitiveness via funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13–3503), European Regional Development Funds (FEDER).Xunta de Galicia; IN607A2018/3Xunta de Galicia; ED431G/01,252Xunta de Galicia; ED431D 2017/1

Evaluation of the COVID-19 response in Spain: principles and requirements

A resurgence of COVID-19 infections is occurring in Spain, with some of the worst figures in Europe. In August, 2020, we urged the Spanish Central Government and regional governments to independently evaluate their COVID-19 response to identify areas where public health and the health and social care system need to be improved

Insight on how to assess and improve the response to the COVID-19 pandemic

La pandemia de COVID-19 ha afectado de manera particularmente intensa a España, pese a su nivel de desarrollo y la elogiada solidez de su Sistema Nacional de Salud. Para comprender qué ha pasado e identificar cómo mejorar la respuesta creemos imprescindible una evaluación independiente multidisciplinaria de la esfera sanitaria, política y socioeconómica. En este trabajo proponemos objetivos, principios, metodología y dimensiones a evaluar, además de esbozar el tipo de resultados y conclusiones esperadas. Nos inspiramos en los requerimientos formulados por el panel independiente de la Organización Mundial de la Salud y en las experiencias evaluativas en otros países, y detallamos la propuesta de aspectos multidimensionales que deben valorarse. La idea es comprender aspectos clave en los ámbitos estudiados y su margen de mejora en lo relativo a preparación, gobernanza, marco normativo, estructuras del Sistema Nacional de Salud (atención primaria, hospitalaria y de salud pública), sector de educación, esquemas de protección social, minimización del impacto económico, y marco y reformas en el ámbito laboral para una sociedad más resiliente. En definitiva, buscamos que este ejercicio sirva no solo para el presente, sino también para que en el futuro estemos mejor preparados y con más ágil capacidad de recuperación ante las amenazas pandémicas que puedan surgir.The COVID-19 pandemic has hit Spain particularly hard, despite being a country with a developed economy and being praised for the robustness of its national health system. In order to understand what happened and to identify how to improve the response, we believe that an independent multi-disciplinary evaluation of the health, political and socio-economic spheres is essential. In this piece we propose objectives, principles, methodology and dimensions to be evaluated, as well as outlining the type of results and conclusions expected. Inspired by the requirements formulated by the WHO Independent Panel for Pandemic Preparedness and Response and by experiences in other countries, we detail the multidimensional aspects to be evaluated. The goal is to understand key aspects in the studied areas and their scope for improvement in terms of preparedness, governance, regulatory framework, national health system structures (primary care, hospital, and public health), education sector, social protection schemes, minimization of economic impact, and labour framework and reforms for a more resilient society. We seek to ensure that this exercise serves not only at present, but also that in the future we are better prepared and more agile in terms of our ability to recover from any pandemic threats that may arise.Ayuda referencia: PI 18/01937 del Fondo de Investigación Sanitaria- Instituto de Salud Carlos III, España, con cofinanciación de Fondos FEDER

The need for an independent evaluation of the COVID-19 response in Spain

Spain has been hit hard by COVID-19, with more than 300 000 cases, 28 498 confirmed deaths, and around 44 000 excess deaths, as of Aug 4, 2020. More than 50 000 health workers have been infected, and nearly 20 000 deaths were in nursing homes. With a population of 47 million, these data place Spain among the worst affected countries. Spain is also reported to have one of the best performing health systems in the world and ranks 15th in the Global Health Security index. So how is it possible that Spain now finds itself in this position