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Miguel Angel Tejedor Hernandez

Bio

Miguel Ángel Tejedor Hernández er forsker i helseanalyse ved Nasjonalt senter for e-helseforskning. Han mottok sin ph.d.-grad i naturvitenskap ved UiT Norges arktiske universitet. Han har en mastergrad i ingeniørvitenskap innen telekommunikasjon, og en annen mastergrad i telekommunikasjonsteknologi, begge fra Universitetet i Las Palmas de Gran Canaria (ULPGC).

Hans forskningsinteresser fokuserer på anvendt maskinlæring for helseomsorg. Mer konkret har hans nyeste forskning fokusert på å utvikle algoritmer for blodsukkerkontroll hos pasienter med diabetes type 1 ved hjelp av maskinlæringsteknikker. Tidligere hadde forskningen hans fokus på å utvikle algoritmer for å oppdage hjernekreft ved hjelp av hyperspektralt kamera og maskinlæringsteknikker.

Miguel Angels prosjekter
Prosjekttittel År Tema Prosjektledelse
Kunnskapsoppsummering for implementering av KI i helsetjenesten 2021 - 2022 Helsedata
Maryam Tayefi Nasrabadi
ClinCode Datamaskinstøttet klinisk ICD-10 koding for å forbedre effektiviteten og kvaliteten i helsetjenesten 2021 - 2024 Helsedata
Hercules Dalianis
SMART: Sensordata og maskinlæring for å støtte hormonerstatning av skjoldbruskkjertelen 2024 - 2026 Helsedata
Tjenester for helsepersonell
Miguel Angel Tejedor Hernandez
Miguel Angels publikasjoner i Cristin
Tittel År Kategori
A multinational study on artificial intelligence adoption: Clinical implementers' perspectives 2024 Academic article
Evaluating Deep Q-Learning Algorithms for Controlling Blood Glucose in In Silico Type 1 Diabetes 2023 Academic article
Implementering av kunstig intelligens i norsk helsetjeneste: veien til utbredt bruk 2023 Report
Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators 2022 Academic literature review
Implementation of artificial intelligence in Norwegian healthcare: The road to broad adoption 2022 Report
Data-Driven Robust Control Using Reinforcement Learning 2022 Academic article
Glucose Regulation for In-Silico Type 1 Diabetes Patients Using Reinforcement Learning 2021 Doctoral dissertation
In-silico evaluation of glucose regulation using policy gradient reinforcement learning for patients with type 1 diabetes mellitus 2020 Academic article
Risk-Averse Food Recommendation Using Bayesian Feedforward Neural Networks for Patients with Type 1 Diabetes Doing Physical Activities 2020 Academic article
Controlling Blood Glucose For Patients With Type 1 Diabetes Using Deep Reinforcement Learning - The Influence Of Changing The Reward Function 2020 Popular scientific lecture
Including T1D knowledge in deep reinforcement learning reduces hypoglycemia 2020 Poster
Controlling Blood Glucose For Patients With Type 1 Diabetes Using Deep Reinforcement Learning - The Influence Of Changing The Reward Function 2020 Poster
Reinforcement learning application in diabetes blood glucose control: A systematic review 2020 Academic literature review
In-silico Evaluation of Trust Region Policy Optimization Reinforcement Learning for T1DM Closed-Loop Control 2019 Poster
In-silico Evaluation of Type-1 Diabetes Closed-Loop Control using Deep Reinforcement Learning 2019 Poster
A Decision Support Tool for Optimal Control of Planet Temperature Using Reinforcement Learning 2018 Lecture
A Novel Use of Hyperspectral Images for Human Brain Cancer Detection using In-Vivo Samples 2016 Popular scientific lecture
Brain tumours detection by semi-supervised algorithm combining spectral unmixing and supervised classification using hyperspectral imaging 2016 Masters thesis
Identification of brain tumours by studying the shape and composition of brain tissues using hyperspectral imaging 2015 Masters thesis
Miguel Angels prosjektrapporter
Rapportnr. Tittel Forfatter(e)
2023 01 Implementering av kunstig intelligens i norsk helsetjeneste: veien til utbredt bruk Alexandra Makhlysheva, Luis Marco Ruiz, Therese Svenning, Phuong Dinh Ngo, Miguel Angel Tejedor Hernandez, Anne Torill Nordsletta, Maryam Tayefi Nasrabadi
2022 01 Implementation of artificial intelligence in Norwegian healthcare: The road to broad adoption Alexandra Makhlysheva, Luis Marco Ruiz, Therese Svenning, Phuong Dinh Ngo, Miguel Angel Tejedor Hernandez, Anne Torill Nordsletta, Maryam Tayefi Nasrabadi