Blar i forfatter "Tayefi, Maryam"

Viser treff 1-4 av 4

    • Challenges and opportunities beyond structured data in analysis of electronic health records 

      Tayefi, Maryam; Ngo, Phuong; Chomutare, Taridzo; Dalianis, Hercules; Salvi, Elisa; Budrionis, Andrius; Godtliebsen, Fred (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-02-14)
      Electronic health records (EHR) contain a lot of valuable information about individual patients and the whole population. Besides structured data, unstructured data in EHRs can provide extra, valuable information but the analytics processes are complex, time-consuming, and often require excessive manual effort. Among unstructured data, clinical text and images are the two most popular and important ...

    • Food recommendation using machine learning for physical activities in patients with type 1 diabetes 

      Ngo, Phuong; Tayefi, Maryam; Nordsletta, Anne Torill; Godtliebsen, Fred (Journal article; Tidsskriftartikkel; Peer reviewed, 2019)
      Physical activities have a significant impact on blood glucose homeostasis of patients with type 1 diabetes. Regular physical exercise provides many proven health benefits and is recommended as part of a healthy lifestyle. However, one of the main side effects of physical activities is hypoglycemia (low blood glucose). Fear of hypoglycemia generally leads to the patients not participating in ...

    • Machine Learning in Chronic Pain Research: A Scoping Review 

      Jenssen, Marit Dagny Kristine; Bakkevoll, Per Atle; Ngo, Phuong; Budrionis, Andrius; Fagerlund, Asbjørn Johansen; Tayefi, Maryam; Bellika, Johan Gustav; Godtliebsen, Fred (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-04-02)
      Given the high prevalence and associated cost of chronic pain, it has a significant impact on individuals and society. Improvements in the treatment and management of chronic pain may increase patients’ quality of life and reduce societal costs. In this paper, we evaluate state-of-the-art machine learning approaches in chronic pain research. A literature search was conducted using the PubMed, IEEE ...

    • Risk-Averse Food Recommendation Using Bayesian Feedforward Neural Networks for Patients with Type 1 Diabetes Doing Physical Activities 

      Ngo, Phuong; Tejedor Hernandez, Miguel Angel; Tayefi, Maryam; Chomutare, Taridzo; Godtliebsen, Fred (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-11-12)
      <p><i>Background.</i> Since physical activity has a high impact on patients with type 1 diabetes and the risk of hypoglycemia (low blood glucose levels) is significantly higher during and after physical activities, an automatic method to provide a personalized recommendation is needed to improve the blood glucose management and harness the benefits of physical activities. This paper aims to reduce ...