BIOMEDICAL DATA SCIENCE

BIOMEDICAL DATA SCIENCE

I have studied different types of problems in which Data Analysis can provide better descriptions or predictions of some illness and disorders such as ADHD, happiness, aging, and cancer.

1. A. Mira-Iglesias, J.A. Conejero, and E. Navarro-Pardo. Natural visibility graphs for diagnosing attention deficit hyperactivity disorder (ADHD). Electronic Notes in Discrete Mathematics, 54,337-342 (2016).
   

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2. C. Moret-Tatay, L.G. Lemus-Zuñiga, D. Abad Tortosa, D. Gamermann, A. Vázquez-Martínez, E. Navarro-Pardo, J.A. Conejero. Age slowing down in detection and visual discrimination under varying presentation times. Scand. Journal. Psych. 58:4, 304-311 (2017). doi:10.1111/sjop.12372
   

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3. E. Navarro-Pardo, L. González-Pozo, P. Villacampa-Fernández, J.A. Conejero. Benefits of a dance group intervention on institutionalized elder people: a Bayesian network approach. Appl. Math. Nonlin. Sci. 3:2, 503-512 (2018). doi:10.2478/AMNS.2018.2.00039
4. M.E. Iglesias Martínez, J.M. García-Gómez, C. Sáez-Silvestre, P. Fernández-de-Córdoba, and J.A. Conejero. Feature extraction and similarity of movement detection during seep, based on higher order spectra and entropy of the actigraphy signal: results of the Community Health Study Hispanic /Study of Latinos. Sensors 18:12, E4310 (2018) doi: 10.3390/s18124310.
   

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5. F.J. Pérez-Benito, P. Villacampa-Fernández, J.A. Conejero, J.M. García-Gómez, and E. Navarro-Pardo. A happiness degree predictor using the conceptual data structure for deep learning architectures. Comput Methods Programs Biomed. 168, 59-68 (2019) doi: 10.1016/j.cmpb.2017.11.004.
6. S. Asensio-Cuesta, A. Sánchez-García, J. Alberto Conejero, C. Sáez, A. Rivero-Rodríguez, and Juan M. García-Gómez. Smartphone sensors for monitoring cancer-related quality of life: App, design, EORTC QLQ-30 mapping, and feasibility in healthy subjects.  Int. J. Env. Res & Public Health. 16, 461 (2019). doi:10.3390/ijerph16030461
7. A. Mira-Iglesias, E. Navarro-Pardo, J.A. Conejero. Power-law distribution of natural visibility graphs from reaction times series. Symmetry 11, 563 (2019). doi:10.3390/sym11040563
8. F.J. Pérez Benito, C. Sáez, J.A. Conejero, S. Tortajada, B. Valdivieso, J.M. García-Gómez. Temporal variability analysis reveals biases in electronic health records due to hospital process reengineering interventions over seven years. PLoS ONE 14(8): e0220369 (2019). doi:10.1371/ journal.pone.0220369
9. S. Asensio-Cuesta, J.M. García-Gómez, J.L. Poza-Luján,  and J.A. Conejero. A game-theory method to design job rotation schedules to prevent musculoskeletal disorders based on workers’ preferences and competencies. Int. J. Environ. Res. Public Health 2019, 16, 4666 (2019). doi:10.3390/ijerph16234666
   

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10. F.J. Pérez-Benito, J.A. Conejero C. Sáez, J.M. García-Gómez, E. Navarro-Pardo, L.L. Florencio y C. Fernández-de-las-Peñas. Subgrouping factors influencing migraine intensity in women: a semi-automatic methodology based on machine learning and information geometry. Pain Practice 20(3):297-309 (2020).  doi:10.1111/papr.12854
    

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11. F.J. Pérez-Benito, J.M. García-Gómez, E. Navarro-Pardo, and J.A. Conejero. Community detection based deep neural network (CD-DNN) architectures: a fully automated framework for Likert scales. Math. Meth. Appl. Sci. 43:14, 8290-8301(2020) doi:10.1002/mma.6567
    

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12. C. Sáez, N. Romero, J.A. Conejero, and J.M. García-Gómez. Potential limitations in COVID-19 machine learning due to data source variability: a case study in the nCov2019 dataset. 15:28, 360-364 (2021). doi:10.1093/jamia/ocaa258
13. S. Asensio-Cuesta, V. Blanes-Selva, J.A. Conejero, A. Frigola, M.G. Portolés-Morales, J.F. MErino-Torres, M. Rubio-Almanza, S. Syed-Abdul, Y.C. Li, R. Vilar-Mateo, L. Fernández-Luque, J.M. García-Gómez. A user-centered Chatbot (Wakamola) to collect linked data in populations networks to support studies of overweight and obesity causes: design and pilot study. JMIR Med. Inform. 9(4):e17503 (2021) doi: 10.2196/17503
14. S. Asensio-Cuesta S, V. Blanes-Selva, M. Portolés, J.A. Conejero, J.M. García-Gómez. How the Wakamola chatbot studied a university community’s lifestyle during the COVID-19 confinement. Health Inform. J. 27(2):14604582211017944 (2021). doi:10.1177/14604582211017944
15. M.A. Lozano, O. Garibo i Orts, E. Piñol, M. Rebollo, K. Polotskaya, M.A. García-March, J.A. Conejero, F. Escolano, and N. Oliver. Open data science to fight COVID-19: Winning the 500k XPRIZE Pandemic Response Challenge. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases ECML-PKKD 2021 (pp. 384-399). Springer, Cham.
    

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16. S. Asensio-Cuesta, V. Blanes-Selva, J.A. Conejero, J.M. Garcia-Gomez. A user-centered chatbot to identify and interconnect individual, social, and environmental risk factors related to overweight and obesity. Inform. Health Soc. Care 47(1):38-52 (2022) DOI: 10.1080/17538157.2021.1923501
17. L. Zhou, N. Romero, J. Martínez-Miranda, J.A. Conejero, J.M. García-Gómez, & C. Sáez. Subphenotyping of Mexican patients with COVID-19 at preadmission to anticipate severity stratification: Age-sex unbiased meta-clustering technique. JMIR Public Health Surveill. (jph) 8(3):e30032 (2022) DOI: 10.2196/30032
18. J.A. Conejero and Nuria Oliver. La iniciativa Valencia IA4COVID. Indice: Revista de Estadística y Sociedad 86:33-35 (2022). Link
19. M.A. Lozano, O. Garibo i Orts, E. Piñol, M. Rebollo, K. Polotskaya, M.A. García-March, J.A. Conejero, F. Escolano, and N. Oliver. Open data science to fight COVID-19: Winning the 500k XPRIZE Pandemic Response Challenge. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. Sister Conferences Best Papers (pp. 5304-5308) (2022) https://doi.org/10.24963/ijcai.2022/740
20. T. Soria Comes, S. Asensio, I. Maestu Maiques, A. Sánchez García, M. Martin Ureste, J.A. Conejero, and J.M. García-Gómez. EP10.01-017 Use of Lalaby in lung cancer patients to trace performance from phone sensors and reported outcomes involving quality of life. J. Thorac. Oncol. 17.9:S507-S508 (2022). doi:10.1016/j.jtho.2022.07.894
21. S. Asensio et al. Testing lung cancer patients’ and oncologists’ experience with the Lalaby app for monitoring the quality of life through mobile sensors and integrated questionnaires. Int. J. Hum. Comp. Inter. doi:10.1080/10447318.2022.2121561
22. D. Fuente, D. Hervás, M. Rebollo, J.A. Conejero, and N. Oliver. COVID-19 outbreaks analysis in the Valencian Region of Spain in the prelude of the third wave. Front. Public Health 10:1010124 (2022) doi:10.3389/fpubh.2022.1010124
23. P.A. Solares-Hernández, Ò. Garibo-i-Orts, J.A. Conejero, F. Manzano. Adaptation of the COVASIM model to incorporate non-pharmaceutical interventions: Application to the Dominican Republic during the second wave of COVID-19. Appl. Math. Nonlinear Sci. 8(2):2319-2332 (2023). doi:10.2478/amns.2023.1.00413
24. A. Begga, Ò. Garibo-i-Orts, S. de María-García, F. Escolano, M.A. Lozano, N. Oliver, and J.A. Conejero (2023) Predicting COVID-19 pandemic waves including vaccination data with deep learning. Front. Public Health 11:1279364. doi:10.3389/fpubh.2023.1279364