Lourdes Araujo

National University of Distance Education

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Juan Martínez-Romo

National University of Distance Education

José A. Cuesta

Universidad Carlos III de Madrid

José Ramón Arribas

Hospital Universitario La Paz

Candela Gonzalez

Hospital Universitario 12 De Octubre

M. Mar Alonso

Hospital Universitario de Canarias

Antonio Antela

Complejo Hospitalario Universitario de Santiago

Octavio Arce

Hospital Central de la Defensa Gómez Ulla

Mónica Martínez

Hospital Universitario de Fuenlabrada

Guillermo Pousada

University Hospital Complex Of Vigo

Lucía Patiño

Galicia Sur Biomedical Foundation

Cooperative Institutions

Instituto de Salud Carlos III

128

Centro de Investigación Biomédica en Red

102

Instituto Ramón y Cajal de Investigación Sanitaria

Hospital Universitario Ramón y Cajal

Hospital Universitario La Paz

Universitat de Barcelona

Hospital General Universitario Gregorio Marañón

Hospital Clínico San Carlos

Hospital Universitario 12 De Octubre

Universidad de Alcalá

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Social Experiments in the Mesoscale: Humans Playing a Spatial Prisoner's Dilemma

PLoS ONE (2010)

Jelena Grujić Constanza Fosco Lourdes Araujo José A. Cuesta Ángel Sánchez

Background The evolutionary origin of cooperation among unrelated individuals remains a key unsolved issue across several disciplines. Prominent among the several mechanisms proposed to explain how cooperation can emerge is the existence of a population structure that determines the interactions among individuals. Many models have explored analytically and by simulation the effects of such a structure, particularly in the framework of the Prisoner's Dilemma, but the results of these models largely depend on details such as the type of spatial structure or the evolutionary dynamics. Therefore, experimental work suitably designed to address this question is needed to probe these issues. Methods and Findings We have designed an experiment to test the emergence of cooperation when humans play Prisoner's Dilemma on a network whose size is comparable to that of simulations. We find that the cooperation level declines to an asymptotic state with low but nonzero cooperation. Regarding players' behavior, we observe that the population is heterogeneous, consisting of a high percentage of defectors, a smaller one of cooperators, and a large group that shares features of the conditional cooperators of public goods games. We propose an agent-based model based on the coexistence of these different strategies that is in good agreement with all the experimental observations. Conclusions In our large experimental setup, cooperation was not promoted by the existence of a lattice beyond a residual level (around 20%) typical of public goods experiments. Our findings also indicate that both heterogeneity and a “moody” conditional cooperation strategy, in which the probability of cooperating also depends on the player's previous action, are required to understand the outcome of the experiment. These results could impact the way game theory on graphs is used to model human interactions in structured groups.

10.1371/journal.pone.0013749

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Citations (223)

Local-Based Semantic Navigation on a Networked Representation of Information

PLoS ONE (2012)

José A. Capitán Javier Borge‐Holthoefer Sergio Gómez Juan Martínez-Romo Lourdes Araujo

The size and complexity of actual networked systems hinders the access to a global knowledge of their structure. This fact pushes the problem of navigation to suboptimal solutions, one of them being the extraction of a coherent map of the topology on which navigation takes place. In this paper, we present a Markov chain based algorithm to tag networked terms according only to their topological features. The resulting tagging is used to compute similarity between terms, providing a map of the networked information. This map supports local-based navigation techniques driven by similarity. We compare the efficiency of the resulting paths according to their length compared to that of the shortest path. Additionally we claim that the path steps towards the destination are semantically coherent. To illustrate the algorithm performance we provide some results from the Simple English Wikipedia, which amounts to several thousand of pages. The simplest greedy strategy yields over an 80% of average success rate. Furthermore, the resulting content-coherent paths most often have a cost between one- and threefold compared to shortest-path lengths.

Representation

Similarity (geometry)

Path length

10.1371/journal.pone.0043694

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Citations (22)

Building a framework for fake news detection in the health domain

PLoS ONE (2024)

Juan R. Martinez-Rico Lourdes Araujo Juan Martínez-Romo

Disinformation in the medical field is a growing problem that carries a significant risk. Therefore, it is crucial to detect and combat it effectively. In this article, we provide three elements to aid in this fight: 1) a new framework that collects health-related articles from verification entities and facilitates their check-worthiness and fact-checking annotation at the sentence level; 2) a corpus generated using this framework, composed of 10335 sentences annotated in these two concepts and grouped into 327 articles, which we call KEANE (faKe nEws At seNtence lEvel); and 3) a new model for verifying fake news that combines specific identifiers of the medical domain with triplets subject-predicate-object, using Transformers and feedforward neural networks at the sentence level. This model predicts the fact-checking of sentences and evaluates the veracity of the entire article. After training this model on our corpus, we achieved remarkable results in the binary classification of sentences (check-worthiness F1: 0.749, fact-checking F1: 0.698) and in the final classification of complete articles (F1: 0.703). We also tested its performance against another public dataset and found that it performed better than most systems evaluated on that dataset. Moreover, the corpus we provide differs from other existing corpora in its duality of sentence-article annotation, which can provide an additional level of justification of the prediction of truth or untruth made by the model.

Binary classification

Predicate (mathematical logic)

10.1371/journal.pone.0305362

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Discovering HIV related information by means of association rules and machine learning

Scientific Reports (2022)

Lourdes Araujo Juan Martínez-Romo Otilia Bisbal Ricardo Sanchez-de-Madariaga Joaquín Portilla

Abstract Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS—so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts.

Association (psychology)

10.1038/s41598-022-22695-y

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Citations (2)