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Ernestina Menasalvas

Universidad Politécnica de Madrid

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Co-Authors

Alejandro Rodríguez‐González

Software (Spain)

Massimiliano Zanin

Institute for Cross-Disciplinary Physics and Complex Systems

Mariano Provencio

Hospital Universitario Puerta de Hierro Majadahonda

Consuelo Gonzalo‐Martín

Universidad Politécnica de Madrid

Lucía Prieto Santamaría

Universidad Politécnica de Madrid

María Torrente

Hospital Universitario Puerta de Hierro Majadahonda

Pedro Sousa

Instituto Federal de Educação, Ciência e Tecnologia do Ceará

Santiago Eibe

Universidad Politécnica de Madrid

Gerardo Lagunes García

Universidad Politécnica de Madrid

Juan Manuel Tuñas

Universidad Politécnica de Madrid

Cooperative Institutions

Universidad Politécnica de Madrid

Hospital Universitario Puerta de Hierro Majadahonda

Centre de Coopération Internationale en Recherche Agronomique pour le Développement

Universidad Complutense de Madrid

Universidad Autónoma de Madrid

Université de Montpellier

Agropolis International

Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement

Institut Agro Montpellier

Instituto de Salud Carlos III

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Towards Treatment Patterns Validation in Lung Cancer Patients

2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA) (2021)

Arturo Redondo Belén Ríos‐Sánchez Guillermo Vigueras Belén Otero-Carrasco Roberto Hernendez

Lung cancer is the leading cause of cancer death. From the estimation of cases that will be in 2021, more than 230,000 new cases are expected to be of lung cancer patients, with an estimation of more than 131,000 deaths. Improving the survival rates or the patient's quality of life is partially covered by a common element: treatments. Collective knowledge about cancer treatment recommendations is typically included in clinical guidelines, intended to optimize patient care and assist clinicians in lung cancer treatment. These guidelines define a set of treatment paths, where recommendations depend on cancer disease aspects and individual features for a concrete patient. Although oncologists are expected to follow clinical guidelines, the inter and intrapatients' variability of response to the possible treatment combinations, makes it necessary to personalize different treatment-patterns on certain cases. Additionally, clinical guidelines are not frequently updated with new findings or lack a consistent methodology when they are frequently updated. For that reason, the analysis of patterns on both patients treated following the standard of care, or outside it, would allow to validate clinical guidelines and identify potential new treatment recommendations. In this work, we have analysed whether actual treatments prescribed to lung cancer patients follow clinical guidelines or not. Using a machine learning method that provides as output association rules (Apriori), we identify patterns based on cancer stage. These preliminary results show that treatments patterns found mostly match with clinical guidelines recommendations, validating the information included in the consulted guidelines.

10.1109/dsaa53316.2021.9564176

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Creating a Metamodel Based on Machine Learning to Identify the Sentiment of Vaccine and Disease-Related Messages in Twitter: the MAVIS Study

Alejandro Rodríguez‐González Juan Manuel Tuñas Diego Fernández Peces-Barba Ernestina Menasalvas Almudena Jaramillo

MAVIS was a project that aimed to study the interactions in social networks (Twitter and Instagram) between users regarding the sentiment expressed in their messages when they talked about specific vaccines or diseases. The study was performed during the period 2015-2018 and was initially technically done by using a set of commercial tools to identify the polarity of the messages. With the aim of improving the results provided by such tools, we performed a deep analysis of the results from such tools and provide a machine learning method as a metamodel over the results of the commercial tools. In this paper we explain both the technical process performed together with the main results that were obtained.

Metamodeling

Sentiment Analysis

10.1109/cbms49503.2020.00053

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Using the Apriori Algorithm to Improve Rough Sets Results

Lecture notes in computer science (2001)

María C. Fernandez-Baizán Ernestina Menasalvas Jose Maria Peña Sanchez Juan Francisco Martínez Sarrías Socorro Millán

10.1007/3-540-45554-x_35

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Completing missing MeSH code mappings in UMLS through alternative expert-curated sources

bioRxiv (Cold Spring Harbor Laboratory) (2019)

Eduardo P. García del Valle Gerardo Lagunes García Lucía Prieto Santamaría Massimiliano Zanin Alejandro Rodríguez‐González

Abstract The increasing availability of biological, clinical and literary sources enables the study of diseases from a more comprehensive approach. However, the interoperability of these sources, particularly of the codes used to identify diseases, poses a major challenge. Because of its role as a hub of multiple medical vocabularies, the Unified Medical Language System (UMLS) has become one of the most widely used resources for mapping diseases from different classification systems. The coverage of these mappings, nevertheless, is still limited, so researchers must resort to other methods to fill these gaps. In this article we analyze the limitations in UMLS mappings for the main disease vocabularies and propose the exploitation of some alternative expert-curated sources to complete it. As a result, we demonstrate that this approach allows resolving more than 50% of the missing mappings for some vocabularies. All the findings are shared for validation and reuse.

Controlled vocabulary

Code (set theory)

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Combining complex networks and data mining: why and how

bioRxiv (Cold Spring Harbor Laboratory) (2016)

Massimiliano Zanin David Papo Pedro Sousa Ernestina Menasalvas A. Nicchi

Abstract The increasing power of computer technology does not dispense with the need to extract meaningful in-formation out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now commonly used in the scientific community: data mining and complex network theory. Not only do complex network analysis and data mining share the same general goal, that of extracting information from complex systems to ultimately create a new compact quantifiable representation, but they also often address similar problems too. In the face of that, a surprisingly low number of researchers turn out to resort to both methodologies. One may then be tempted to conclude that these two fields are either largely redundant or totally antithetic. The starting point of this review is that this state of affairs should be put down to contingent rather than conceptual differences, and that these two fields can in fact advantageously be used in a synergistic manner. An overview of both fields is first provided, some fundamental concepts of which are illustrated. A variety of contexts in which complex network theory and data mining have been used in a synergistic manner are then presented. Contexts in which the appropriate integration of complex network metrics can lead to improved classification rates with respect to classical data mining algorithms and, conversely, contexts in which data mining can be used to tackle important issues in complex network theory applications are illustrated. Finally, ways to achieve a tighter integration between complex networks and data mining, and open lines of research are discussed.

Representation

Complex system

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Public Health and Epidemiology Informatics: Can Artificial Intelligence Help Future Global Challenges? An Overview of Antimicrobial Resistance and Impact of Climate Change in Disease Epidemiology

Yearbook of Medical Informatics (2019)

Alejandro Rodríguez‐González Massimiliano Zanin Ernestina Menasalvas

Objectives: To provide an oveiview of the current application of artificial intelligence (AI) in the field of public health and epidemiology, with a special focus on antimicrobial resistance and the impact of climate change in disease epidemiology. Both topics are of vital importance and were included in the “Ten threats to global health in 2019“ report published by the World Health Organization. Methods: We analysed publications that appeared in the last two years, between January 2017 and October 2018. Papers were searched using Google Scholar with the following keywords: public health, epidemiology, machine learning, data analytics, artificial intelligence, disease surveillance, climate change, antimicrobial resistance, and combinations thereof. Selected articles were organised by theme. Results: In spite of a large interest in AI generated both within and outside the scientific community, and of the many opinions pointing towards the importance of a better use of data in public health, few papers have been published on the selected topics in the last two years. We identify several potential reasons, including the complexity of the integration of heterogeneous data, and the lack of sound and unbiased validation procedures. Conclusions: As there is a better comprehension of AI and more funding available, artificial intelligence will become not only the centre of attention in informatics, but more importantly the source of innovative solutions for public health.

Global Health

Public Health Informatics

10.1055/s-0039-1677910

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Citations (45)

CRM in e-Business: a Client’s Life Cycle Model Based on a Neural Network

Studies in fuzziness and soft computing (2002)

Óscar Marbán Ernestina Menasalvas César Montes John G. Rajakulendran Javier Segovia

10.1007/978-3-7908-1779-9_5

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Dynamic Clustering Process to Calculate Affinity Degree of Users as Basis of a Social Network Recommender

Lecture notes in computer science (2013)

Andrea Zanda Ernestina Menasalvas Santiago Eibe

Component (thermodynamics)

Information filtering system

Social network (sociolinguistics)

10.1007/978-3-642-38333-5_22

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A Report of Activities at the WIC-Spain Research Centre

Web Intelligence (2004)

Pilar Herrero Marı́a S. Pérez Ernestina Menasalvas Javier Segovia

The WIC(Web Intelligence Consortium)-Spain Research Centre is an interdisciplinary research initiative which brings together people with the aim of covering all the aspects related to the discovery of hidden knowledge in big volumes of data as long as exploring the practical impacts of advanced Information Technology (IT) and Artificial Intelligence (AI) on this volume of information The group consist of seven researchers and five Ph.D. students led by Dr. Javier Segovia and coordinated by Dr. Ernestina Menasalvas. We are funded from a variety of sources including the Spanish Ministry of Science and Technology as well as national and international industry.

Christian ministry

10.1109/wi.2004.10

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Preprocessing and analyzing genetic data with complex networks: An application to Obstructive Nephropathy

Networks and Heterogeneous Media (2012)

Massimiliano Zanin Ernestina Menasalvas Pedro Sousa Stefano Boccaletti

Many diseases have a genetic origin, and a great effort is being made to detect the genes that are responsible for their insurgence. One of the most promising techniques is the analysis of genetic information through the use of complex networks theory. Yet, a practical problem of this approach is its computational cost, which scales as the square of the number of features included in the initial dataset. In this paper, we propose the use of an iterative feature selection strategy to identify reduced subsets of relevant features, and show an application to the analysis of congenital Obstructive Nephropathy. Results demonstrate that, besides achieving a drastic reduction of the computational cost, the topologies of the obtained networks still hold all the relevant information, and are thus able to fully characterize the severity of the disease.

Feature (linguistics)

Data pre-processing

10.3934/nhm.2012.7.473

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