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Forsker Department of Mathematics and Statistics tejedor.h.angel@uit.no

Miguel Angel Tejedor Hernandez

  • Phuong Dinh Ngo, Miguel Angel Tejedor Hernandez, Therese Olsen Svenning, Taridzo Fred Chomutare, Andrius Budrionis, Hercules Dalianis :
    Deidentifying a Norwegian clinical corpus - An effort to create a privacy-preserving Norwegian large clinical language model
    Proceedings of the Workshop on Computational Approaches to Language Data Pseudonymization (CALD-pseudo 2024) 2024 ARKIV
  • Marit Dagny Kristine Jenssen, Elisa Salvi, Egil Andreas Fors, Ole Andreas Nilsen, Phuong Dinh Ngo, Miguel Angel Tejedor Hernandez et al.:
    Exploring Pain Reduction through Physical Activity: A Case Study of Seven Fibromyalgia Patients
    Bioengineering 2024 ARKIV / DOI
  • Luis Marco Ruiz, Miguel Angel Tejedor Hernandez, Phuong Dinh Ngo, Alexandra Makhlysheva, Therese Olsen Svenning, Kari Dyb et al.:
    A multinational study on artificial intelligence adoption: Clinical implementers' perspectives
    International Journal of Medical Informatics 2024 DOI
  • Tejedor H Miguel Angel, Sigurd Hjerde, Jonas Nordhaug Myhre, Fred Godtliebsen :
    Evaluating Deep Q-Learning Algorithms for Controlling Blood Glucose in In Silico Type 1 Diabetes
    Diagnostics (Basel) 07. October 2023 ARKIV / DOI
  • Phuong Ngo, Miguel Angel Tejedor Hernandez, Fred Godtliebsen :
    Data-Driven Robust Control Using Reinforcement Learning
    Applied Sciences 2022 FULLTEKST / ARKIV / DOI
  • Taridzo Fred Chomutare, Miguel Angel Tejedor Hernandez, Therese Olsen Svenning, Luis Marco Ruiz, Maryam Tayefi Nasrabadi, Karianne Fredenfeldt Lind et al.:
    Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators
    International Journal of Environmental Research and Public Health (IJERPH) 2022 ARKIV / DOI
  • Miguel Angel Tejedor Hernandez, Ashenafi Zebene Woldaregay, Fred Godtliebsen :
    Reinforcement learning application in diabetes blood glucose control: A systematic review
    Artificial Intelligence in Medicine 2020 ARKIV / DOI
  • Phuong Ngo, Miguel Angel Tejedor Hernandez, Maryam Tayefi, Taridzo Chomutare, Fred Godtliebsen :
    Risk-Averse Food Recommendation Using Bayesian Feedforward Neural Networks for Patients with Type 1 Diabetes Doing Physical Activities
    Applied Sciences 2020 ARKIV / DOI
  • Jonas Nordhaug Myhre, Miguel Angel Tejedor Hernandez, Ilkka Kalervo Launonen, Anas El Fathi, Fred Godtliebsen :
    In-silico evaluation of glucose regulation using policy gradient reinforcement learning for patients with type 1 diabetes mellitus
    Applied Sciences 11. September 2020 ARKIV / DOI
  • Alexandra Makhlysheva, Luis Marco Ruiz, Therese Olsen Svenning, Phuong Ngo, Miguel Angel Tejedor Hernandez, Anne Torill Nordsletta et al.:
    Implementering av kunstig intelligens i norsk helsetjeneste: veien til utbredt bruk
    2023 FULLTEKST
  • Alexandra Makhlysheva, Luis Marco Ruiz, Therese Olsen Svenning, Phuong Ngo, Miguel Angel Tejedor Hernandez, Maryam Tayefi Nasrabadi et al.:
    Implementation of artificial intelligence in Norwegian healthcare: The road to broad adoption
    2022 FULLTEKST
  • Tejedor H Miguel Angel :
    Glucose Regulation for In-Silico Type 1 Diabetes Patients Using Reinforcement Learning
    UiT Norges arktiske universitet 2021
  • Miguel Angel Tejedor Hernandez, Jonas Nordhaug Myhre :
    Including T1D knowledge in deep reinforcement learning reduces hypoglycemia
    2020
  • Miguel Angel Tejedor Hernandez, Jonas Nordhaug Myhre :
    Controlling Blood Glucose For Patients With Type 1 Diabetes Using Deep Reinforcement Learning - The Influence Of Changing The Reward Function
    2020
  • Miguel Angel Tejedor Hernandez, Jonas Nordhaug Myhre :
    Controlling Blood Glucose For Patients With Type 1 Diabetes Using Deep Reinforcement Learning - The Influence Of Changing The Reward Function
    2020
  • Jonas Nordhaug Myhre, Miguel Angel Tejedor Hernandez, Ilkka Kalervo Launonen, Fred Godtliebsen :
    In-silico Evaluation of Trust Region Policy Optimization Reinforcement Learning for T1DM Closed-Loop Control
    2019
  • Jonas Nordhaug Myhre, Miguel Angel Tejedor Hernandez, Ilkka Kalervo Launonen, Fred Godtliebsen :
    In-silico Evaluation of Type-1 Diabetes Closed-Loop Control using Deep Reinforcement Learning
    2019
  • Phuong D. Ngo, Miguel Angel Tejedor Hernandez, Fred Godtliebsen :
    A Decision Support Tool for Optimal Control of Planet Temperature Using Reinforcement Learning
    2018
  • Himar Fabelo, Samuel Ortega, R. Guerra, Gustavo Callico, A. Szolna, J. F. Piñeiro et al.:
    A Novel Use of Hyperspectral Images for Human Brain Cancer Detection using In-Vivo Samples
    2016
  • Miguel Angel Tejedor Hernandez :
    Brain tumours detection by semi-supervised algorithm combining spectral unmixing and supervised classification using hyperspectral imaging
    Universidad de las Palmas de Gran Canaria 2016
  • Miguel Angel Tejedor Hernandez :
    Identification of brain tumours by studying the shape and composition of brain tissues using hyperspectral imaging
    Universidad de las Palmas de Gran Canaria 2015

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