AI applications in healthcare and biomedicine

The CAOS lab specializes in the applications of machine learning and big data technologies in in healthcare and biomedicine. .

RESEARCH LINES

CAOS Lab has several research lines in the field of artificial intelligence applications to healthcare,
biomedicine and active aging:

• Minimal residual disease monitoring in cancer.
• Surgical phase recognition from videos using convolutional neural networks and attention
  models.
• Predictive models in healthcare, with applications in subarachnoid haemorrhage, cranial trauma, ICU monitoring time series and actigraphy time series.
• AI for eHealth systems: intelligent remote patient follow-up using wound imaging and self-reported patient data after surgery

• Stress recognition from biomedical signals.
• Electronic healthcare record representations using deep-learning based models.
• Ambient Assisted living, systems and algorithms for behavioural monitoring, activity
  recognition and anomaly detection for elderly patients living alone.

PUBLICATIONS (SUMMARY)

• Ana Jiménez Ubieto; Alejandro Martín Muñoz; María Poza; Sara Dorado Alfaro; Almudena
  García Ortiz; Enrique Revilla; Pilar Sarandeses; Yanira Ruiz Heredia; Tycho Baumann; Antonia
  Rodríguez; María Calbacho; Pilar Martínez Sánchez; José María Sánchez Pina; Chongwu Wang;
  María Teresa Cedena; Alejandro Martín García-Sancho; Gloria Figaredo; Daniel Gil Alós; Laura
  Rufián; Margarita Rodríguez; Laura Carneros; Carolina Martinez Laperche; Mariana Bastos
  Oreiro; Inmaculada Rapado; De Toledo Heras, Paula; Miguel Gallardo; Antonio Valeri; Rosa
  Ayala; Joaquín Martínez López; Santiago Barrio A. Personalized monitoring of circulating tumor
  DNA with a specific signature of trackable mutations after chimeric antigen receptor T-cell
  therapy in follicular lymphoma patients. Frontiers in Immunology. 2023. DOI:
  10.3389/fimmu.2023.1188818
• Jimenez Ubieto Ana; Poza Maria; Martin Muñoz Alejandro; Ruiz Heredia Yanira; Dorado Sara;
  Figaredo Gloria; Rosa Rosa Juan Manuel; Rodriguez Antonia; Barcena Carmen; Navamuel Laura
  Parrilla; Carrillo Jaime; Sanchez Ricardo; Rufian Laura; Juarez Alexandra; Rodriguez
  Margarita; Wang Chongwu. De Toledo Paula; Grande Carlos; Mollejo Manuela; Casado Luis
  Felipe; Calbacho Maria; Baumann Tycho; Rapado Inmaculada; Gallardo Miguel; Sarandeses
  Pilar; Ayala Rosa; Martinez Lopez Joaquin; Barrio Santiago.. Real-life disease monitoring in
  follicular lymphoma patients using liquid biopsy ultra-deep sequencing and PET/CT. Leukemia.
  2023. DOI: https://doi.org/10.1038/s41375-022-01803-x
• Ricardo Sánchez, Sara Dorado, Yanira Ruíz-Heredia, Alejandro Martín-Muñoz, Juan Manuel
  Rosa-Rosa, Jordi Ribera, Olga García, Ana Jimenez-Ubieto, Gonzalo Carreño-Tarragona, María
  Linares, Laura Rufián, Alexandra Juárez, Jaime Carrillo, María José Espino, Mercedes Cáceres,
  Sara Expósito, Beatriz Cuevas, Raúl Vanegas, Luis Felipe Casado, Anna Torrent, Lurdes
  Zamora, Santiago Mercadal, Rosa Coll, Marta Cervera, Mireia Morgades, José Ángel Hernández-
  Rivas, Pilar Bravo, Cristina Serí, Eduardo Anguita, Eva Barragán, Claudia Sargas, Francisca
  Ferrer-Marín, Jorge Sánchez-Calero, Julián Sevilla, Elena Ruíz, Lucía Villalón, María del Mar
  Herráez, Rosalía Riaza, Elena Magro, Juan Luis Steegman, Chongwu Wang, Paula De Toledo ,
  Valentín García-Gutiérrez, Rosa Ayala, Josep-Maria Ribera, Santiago Barrio, Joaquín Martínez-
  López. Detection of kinase Domain mutations in BCR::ABL1 leukemia by ultra-deep sequencing
  of genomic DNA. Scientific Reports. 2022. DOI: https://doi.org/10.1038/s41598-022-17271-3
• P. de Toledo, C. Joppien, M. P. Sesmero and P. Drews. Mining Disease Courses across
  Organizations: A Methodology Based on Process Mining of Diagnosis Events Datasets. Annual
  International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 2019.
  DOI: 10.1109/EMBC.2019.8857149
• P. de Toledo, R. Pérez-Rodríguez, P. de Miguel, A. Sanchis and P. Serrano. Prediction of patient evolution in terms of Clinical Risk Groups form routinely collected data using machine learning. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 2019. DOI: 10.1109/EMBC.2019.8857625
• Jaime Delgado, Arriel Benis, Paula de Toledo, Parisis Gallos, Mauro Giacomini, Alicia Martinez-García, Dario Salvi: Applying the FAIR Principles to Accelerate Health Research in Europe in the Post COVID-19 Era - Proceedings of the 2021 EFMI Special Topic Conference,
• EFMI-STC 2021, Virtual Event / Seville, Spain, 22 - 24 November 2021. Studies in Health Technology and Informatics 287, IOS Press 2021, ISBN 978-1-64368-236-5
• Ordonez, FJ; Englebienne, G; de Toledo, P; van Kasteren, T; Sanchis, A; Krose, B. In-Home Activity Recognition: Bayesian Inference for Hidden Markov Models. Author(s). IEEE Pervasive Computing Volume: 13 Issue: 3 Pages: 67-75. 2014. http://doi.ieeecomputersociety.org/10.1109/MPRV.2014.52
• FJ Ordóñez, P de Toledo, A Sanchis. Sensor-based Bayesian detection of anomalous living patterns in a home setting. Personal and Ubiquitous Computing, Springer. February 2015, Volume 19, Issue 2, pp 259-270 (2015). doi10.1007/s00779-014-0820-1
• Jimenez-Fernandez, S., De Toledo, P., Del Pozo, F. Usability and interoperability in wireless sensor networks for patient telemonitoring in chronic disease management. IEEE Transactions on Biomedical Engineering, (2013) 60 (12), art. no. 6589183, pp. 3331-3339.
• Javier Ordóñez, F., de Toledo, P., Sanchis, A. Activity recognition using hybrid generative/discriminative models on home environments using binary sensors. (2013) Sensors (Switzerland), 13 (5), pp. 5460-5477.
• Ordonez FJ , J Iglesias JA, de Toledo P, Ledezma A, Sanchis A. Online Activity Recognition using Evolving Classifiers Expert Systems With Applications. 2013. Vol 40(4). 1248–1255 March 2013, http://dx.doi.org/10.1016/j.eswa.2012.08.066

RESEARCH PROJECTS

• SurgeryAI: Incorporando herramientas digitales e IA en la cadena de valor de la cirugía. 2022-
  2024. Agencia Estatal de Investigación. (enlace a https://www.uc3m.es/investigacion/aisurgery).
• Análisis de los patrones de sueño y actividad con técnicas de Inteligencia Artificial. Fundación Investigación Biomédica Hospital 12 Octubre. 2023-2024.
• Desarrollo e implantación de estrategias de análisis genéticos basadas en inteligencia.
  artificial, aprendizaje automático y big data, para la creación de un test universal que permita
  detectar y cuantificar la enfermedad mínima residual en cualquier paciente con cáncer.
  Comunidad de Madrid. Conserjería de Educación e Investigación. 2021-2023
• PreDiCT-TB. Model-based preclinical development of anti-tuberculosis drug combinations. European Commission. Innovative Medicines Initiative (IMI). 2012-2017
• Topus: Tomografía por Emisión de Positrones y Ultrasonidos. S2013/MIT-3024. Programa de
  Actividades de I+D en Tecnologías. Comunidad de Madrid
• Trainutri. Training and Nutrition senior social platform. European Commission. Ambient
  assisted living joint programme. 2011-2013

RESEARCH COLLABORATIONS

The CAOS lab has stable collaborations with the following institutions:

• Hospital 12 de Octubre de Madrid
• Altum sequencing.
• Institute of Biomedical Engineering. University of Oxford.
• Integrating the Healthcare Enterprise standard development Organization.
• Faculty of Technology and Society. Malmo University.