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).

  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

  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.

  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

    F.J. Pérez Benito et al. Temporal variability analysis reveals biases in electronic health records due to hospital process reengineering interventions over seven years. PLoS ONE

    F.J. Pérez Benito et al.  PLoS ONE (2019)

  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

  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 Medicine 20(3):297-309 (2020).  doi:10.1111/papr.12854

  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.6567Community detection and neural networks.
  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. To appear, JAMIA. doi:10.1093/jamia/ocaa258