Hey, I'm Ariel
Guerra-Adames

News

• 17/12/2024: My paper "Uncovering Judgment Biases in Emergency Triage: A Public Health Approach Based on Large Language Models" won Best Paper on the "Impact and Society" track at ML4H!
• 16/12/2024: I will be presenting my paper "Uncovering Judgment Biases in Emergency Triage: A Public Health Approach Based on Large Language Models" with a Spotlight Presentation and a poster at ML4H 2024 in Vancouver!
• 28/10/2024: I participated in the Autumn School XIHO24: Experimentation of Human-Object interactions in Bordeaux.
• 23/10/2024: We got a new publication accepted in JMIR AI - "Harnessing Moderate-Sized Language Models for Reliable Patient Data De-identification in Emergency Department Records".

Research Interests

My main research interest is in how we can use language models to quantify human biases in emergency medical decision-making. In other words, I'm interested in fairness but for humans. I have worked on several projects related to the analysis of long-unstructured text for different tasks ranging from sentiment analysis to topic modeling, and more recently, text classification on data from social media and the health domain.
I have also explored the application of Brain-Computer Interfaces with non-invasive electrodes during my Engineering thesis. This work consisted in the online processing of non-stationary EEG signals for the control of a device.
There's more to it than that however, if you have any questions regarding my past work, don't hesitate to ask!

Publications

* = Equal contribution

• AI-Driven Emergency Patient Flow Optimization is Both an Unmissable Opportunity and a Risk of Systematizing Health Disparities
  Dylan Russon, Marta Avalos-Fernandez, Ariel Guerra-Adames, Cédric Gil-Jardiné and Emmanuel Lagarde
  The International FLAIRS Conference Proceedings, 37(1). 2024
  
  Abstract PDF BibTeX
  There is a burgeoning interest in harnessing artificial intelligence (AI) to enhance patient flow within emergency departments (EDs). However, this advancement is accompanied by a significant risk: by relying on historical healthcare data, these AI tools may perpetuate existing systemic biases associated with gender, age, ethnicity, and socioeconomic status. This paper surveys studies identifying biases in ED data, offering context for concern about these biases. These insights are valuable for researchers developing AI to optimize ED workflows while accounting for ethical considerations.
  @inproceedings{russon2024ai, title={AI-Driven Emergency Patient Flow Optimization is Both an Unmissable Opportunity and a Risk of Systematizing Health Disparities: Human Bias in ED: Implications for AI-Driven Patient Flow Optimization}, author={Russon, Dylan and Avalos, Marta and Guerra-Adames, Ariel and Gil-Jardin{\'e}, C{\'e}dric and Lagarde, Emmanuel}, booktitle={The International FLAIRS Conference Proceedings}, volume={37}, year={2024} }
  
  
• Harnessing Moderate-Sized Language Models for Reliable Patient Data De-identification in Emergency Department Records: An Evaluation of Strategies and Performance (Preprint)
  Océane Dorémus, Dylan Russon, Benjamin Contrand, Ariel Guerra-Adames, Marta Avalos-Fernandez, Cédric Gil-Jardiné et Emmanuel Lagarde
  JMIR AI, 2024.
  
  Abstract PDF BibTeX
  The digitization of healthcare, facilitated by the adoption of electronic health record (EHR) systems, has revolutionized data-driven medical research and patient care. While this digital transformation offers substantial benefits in healthcare efficiency and accessibility, it concurrently raises significant concerns over privacy and data security. Initially, the journey towards protecting patient data de-identification saw the transition from rule-based systems to more mixed approaches including machine learning for de-identifying patient data. Subsequently, the emergence of Large Language Models (LLMs) has represented a further opportunity in this domain, offering unparalleled potential for enhancing the accuracy of context-sensitive de-identification. However, despite LLMs offering significant potential, the deployment of the most advanced models in hospital environments is frequently hindered by data security issues and the extensive hardware resources required.
  @article{doremus2024harnessing, title={Harnessing Moderate-Sized Language Models for Reliable Patient Data De-identification in Emergency Department Records: An Evaluation of Strategies and Performance}, author={Dorémus, Océane and Russon, Dylan and Contrand, Benjamin and Guerra-Adames, Ariel and Avalos-Fernandez, Marta and Gil-Jardiné, Cédric and Lagarde, Emmanuel}, journal={JMIR AI}, year={2024}, }
  
  
• Analyzing Spanish-Language Public Sentiment in the Context of a Pandemic and Social Unrest: The Panama Case
  Fernando Arias*, Ariel Guerra-Adames*, Maytee Zambrano, Efraín Quintero-Guerra and Nathalia Tejedor-Flores.
  International Journal of Environmental Research and Public Health. 2022.
  
  Abstract PDF BibTeX
  Over the past decade, an increase in global connectivity and social media users has changed the way in which opinions and sentiments are shared. Platforms such as Twitter can act as public forums for expressing opinions on non-personal matters, but often also as an outlet for individuals to share their feelings and personal thoughts. This becomes especially evident during times of crisis, such as a massive civil disorder or a pandemic. This study proposes the estimation and analysis of sentiments expressed by Twitter users of the Republic of Panama during the years 2019 and 2020. The proposed workflow is comprised of the extraction, quantification, processing and analysis of Spanish-language Twitter data based on Sentiment Analysis. This case of study highlights the importance of developing natural language processing resources explicitly devised for supporting opinion mining applications in Latin American countries, where language regionalisms can drastically change the lexicon on each country. A comparative analysis performed between popular machine learning algorithms demonstrated that a version of a distributed gradient boosting algorithm could infer sentiment polarity contained in Spanish text in an accurate and time-effective manner. This algorithm is the tool used to analyze over 20 million tweets produced between the years of 2019 and 2020 by residents of the Republic of Panama, accurately displaying strong sentiment responses to events occurred in the country over the two years that the analysis performed spanned. The obtained results highlight the potential that methodologies such as the one proposed in this study could have for transparent government monitoring of responses to public policies on a population scale.
  @article{arias2022analyzing, title={Analyzing Spanish-Language Public Sentiment in the Context of a Pandemic and Social Unrest: The Panama Case}, author={Arias, Fernando and Guerra-Adames, Ariel and Zambrano, Maytee and Quintero-Guerra, Efra{\'\i}n and Tejedor-Flores, Nathalia}, journal={International Journal of Environmental Research and Public Health}, volume={19}, number={16}, pages={10328}, year={2022}, publisher={MDPI} }
  
  
• Sentiment Analysis of Public Social Media as a Tool for Health-Related Topics
  Fernando Arias, Maytee Zambrano, Ariel Guerra-Adames, Nathalia Tejedor-Flores and Miguel Vargas-Lombardo.
  IEEE Access. June 2022.
  
  Abstract PDF BibTeX
  For decades, researchers have experimented with the possibility that machines can equal human linguistic capabilities. Recently, advances in the field of natural language processing (NLP) as well as a substantial increase in available naturally occurring linguistic data on social media platforms have made more advanced methodologies such as sentiment analysis (SA) gain substantial momentum on contemporary applications. This document compiles what the authors consider to be some of the most important concepts related to SA, as well as techniques and processes necessary for the various stages of its implementation. Furthermore, specific applications related to the extraction and classification of social media data using novel SA techniques are presented and quantified, with an emphasis on those used for the identification of mental health degradation during the COVID-19 pandemic. Finally, the authors present several conclusions highlighting the most prominent benefits and drawbacks of the methods discussed, followed by a brief discussion of possible future applications of certain methods of interest.
  @article{arias2022sentiment, title={Sentiment analysis of public social media as a tool for health-related topics}, author={Arias, Fernando and Nunez, Maytee Zambrano and Guerra-Adames, Ariel and Tejedor-Flores, Nathalia and Vargas-Lombardo, Miguel}, journal={IEEE Access}, volume={10}, pages={74850--74872}, year={2022}, publisher={IEEE} }
  
  
• Control of a Simulated Terrestrial Vehicle Through the Real-Time Processing and Classification of Electroencephalogram Data
  Ariel Guerra-Adames, Danilo Cáceres and Fernando Merchan.
  Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE). March 2022.
  
  Abstract PDF BibTeX
  In recent years and in parallel with advances in commercially available computing power, the brain computer interface (BCI) has emerged as a new use for the information obtained from existing electroencephalogram (EEG) acquisition technologies. This system works as a communication pathway between an individual’s central nervous system and a computing unit. The BCI described in this study has as primary directive the control of a simulated vehicle, for which it utilizes both neurological activity associated to an individual’s state of focus in the frequency domain, as well as artifacts produced by intentional blinks, found in the time domain. In order to successfully translate the user’s intention into commands, the acquired signals are spectrally and temporally pre-processed, features of interest are extracted by means of the wavelet packet decomposition (WPD), and are finally fed into a convolutional neural network (CNN) model, which outputs recursive predictions about the user’s mental state. The set of these general steps is capable of emitting navigational commands to the vehicle, and at the same time, return a user’s focus state related to the task in hand in the form of a visual feedback loop. Tests performed with the complete system, demonstrate that a system as the one proposed is satisfactorily functional, being capable of carrying the user’s intent to the simulated vehicle with a latency and margin of error within what’s expected for BCI applications based on EEG readings.
  @inproceedings{guerra2022control, title={Control of a Simulated Terrestrial Vehicle Through the Real-Time Processing and Classification of Electroencephalogram Data}, author={Guerra-Adames, A and C{\'a}ceres, D and Merch{\'a}n, F}, booktitle={2022 Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE)}, pages={1--1}, year={2022}, organization={IEEE} }
  
  
• Real Time Operator Focus Monitoring System Based on a Brain-Computer Interface
  Ariel Guerra-Adames, Danilo Cáceres, Fernando Merchan and Kang-Hyun Jo.
  IEEE 29th International Symposium on Industrial Electronics (ISIE). June 2020.
  
  Abstract PDF BibTeX
  Recent advances in applications of Brain-Computer Interfaces (BCIs) for control and communication in more complex systems have demonstrated their potential for the augmentation of human cognition. This paper aims to develop a BrainComputer Interface application capable of monitoring the focus level of a user in real time and produce a binarized output signal based on such electrophysiological activity to show that a real-time, low-cost BCI can be implemented in industrialized settings. This can be achieved by applying a signal processing stage consisting of a DC offset subtraction layer, a bandpass filter bank and a number of thresholds tuned for each individual user. Experiments performed showed that while it is possible to monitor the desired neurological features, a number of undesired artifacts present at all times in brain signals make online analysis especially challenging.
  @inproceedings{guerra2020real, title={Real Time Operator Focus Monitoring System Based on a Brain-Computer Interface}, author={Guerra, Ariel and Caceres, Danilo and Merchan, Fernando and Jo, Kang-Hyun}, booktitle={2020 IEEE 29th International Symposium on Industrial Electronics (ISIE)}, pages={1415--1420}, year={2020}, organization={IEEE} }
  
  
• Bioacoustic classification of Antillean manatee vocalization spectrograms using deep convolutional neural networks
  Fernando Merchan, Ariel Guerra-Adames, Héctor Poveda, Héctor M. Guzman and Javier E. Sanchez-Galan.
  Applied Sciences. May 2020.
  
  Abstract PDF BibTeX
  We evaluated the potential of using convolutional neural networks in classifying spectrograms of Antillean manatee (Trichechus manatus manatus) vocalizations. Spectrograms using binary, linear and logarithmic amplitude formats were considered. Two deep convolutional neural networks (DCNN) architectures were tested: linear (fixed filter size) and pyramidal (incremental filter size). Six experiments were devised for testing the accuracy obtained for each spectrogram representation and architecture combination. Results show that binary spectrograms with both linear and pyramidal architectures with dropout provide a classification rate of 94–99% on the training and 92–98% on the testing set, respectively. The pyramidal network presents a shorter training and inference time. Results from the convolutional neural networks (CNN) are substantially better when compared with a signal processing fast Fourier transform (FFT)-based harmonic search approach in terms of accuracy and F1 Score. Taken together, these results prove the validity of using spectrograms and using DCNNs for manatee vocalization classification. These results can be used to improve future software and hardware implementations for the estimation of the manatee population in Panama.
  @article{merchan2020bioacoustic, title={Bioacoustic classification of Antillean manatee vocalization spectrograms using deep convolutional neural networks}, author={Merchan, Fernando and Guerra, Ariel and Poveda, H{\'e}ctor and Guzm{\'a}n, H{\'e}ctor M and Sanchez-Galan, Javier E}, journal={Applied Sciences}, volume={10}, number={9}, pages={3286}, year={2020}, publisher={MDPI} }

Talks & Presentations

• Dec 2024 Spotlight Presentation "Uncovering Judgment Biases in Emergency Triage: A Public Health Approach Based on Large Language Models" at ML4H Symposium, Vancouver, Canada


• Jul 2024 "Réflexions pour la conception d'un protocole expérimental de détection des biais dans le triage d'urgence hospitalière à l'aide de modèles de langage" at EvalLLM2024, Toulouse, France


• Mar 2024 "Optimisation des services des urgences hospitalières à l'aide de l'IA : une opportunité incontournable et un risque de systématisation des disparités de santé" at Dataquitaine 2024, Bordeaux, France


• Jan 2024 Best Pitch Award "Learning PDEs from Data: Can We Solve the Non-registration Issue?" at Manutech SLEIGHT Science Event #11, Saint-Étienne, France


• Jun 2021 "Control de un Vehículo Mediante el Procesamiento e Interpretación en Tiempo Real de Señales de Electroencefalografía" at APANAC 2021, Panama City, Panama