Imperial College London

DrBernardHernandez Perez

Faculty of MedicineDepartment of Infectious Disease

Research Fellow



b.hernandez-perez Website CV




B420Electrical EngineeringSouth Kensington Campus





Bernard Hernandez is currently a post-doctoral research associate at the Centre for Bio-Inspired Technology at Imperial College London. He received his BSc degree in Telecommunications (5 years) and Computer Science (3 years) from the Rey Juan Carlos University (URJC) in 2013. Simultaneously, he received his MSc degree in Machine Learning (2 years) from the Royal Institute of Technology (KTH). He then moved to Imperial College London (ICL) where he worked as a Research Assistant in the Centre for Bio-Inspired Technology, obtaining his PhD degree in 2019.

His current research interests include the development of point-of-care decision support systems that leverage the existing data and resources to provide personalized, accurate and effective diagnostics focusing specially on detection of infectious diseases and antimicrobial resistance in low- and middle-income countries.



Chanh HQ, Ming DK, Nguyen QH, et al., 2023, Applying artificial intelligence and digital health technologies, Viet Nam, Bulletin of the World Health Organization, Vol:101, ISSN:0042-9686, Pages:487-492

Hernandez Perez B, Stiff O, Ming D, et al., 2023, Learning meaningful latent space representations for patient risk stratification: model development and validation for dengue and other acute febrile illness, Frontiers in Digital Health, Vol:5, ISSN:2673-253X, Pages:1-16

Ming D, Nguyen QH, An LP, et al., 2023, Mapping patient pathways and understanding clinical decision-making in dengue management to inform the development of digital health tools, Bmc Medical Informatics and Decision Making, Vol:23, ISSN:1472-6947, Pages:1-9

Bolton W, Rawson T, Hernandez B, et al., 2022, Machine learning and synthetic outcome estimation for individualised antimicrobial cessation, Frontiers in Digital Health, Vol:4, ISSN:2673-253X, Pages:1-12

Le V-KD, Hai BH, Karolcik S, et al., 2022, vital_sqi: A Python package for physiological signal quality control, Frontiers in Physiology, Vol:13

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