Blar i forfatter Artikler, rapporter og annet (matematikk og statistikk) "Budrionis, Andrius"

Viser treff 1-3 av 3

    • 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 ...

    • De-identifying Swedish EHR text using public resources in the general domain 

      Chomutare, Taridzo; Yigzaw, Kassaye Yitbarek; Budrionis, Andrius; Makhlysheva, Alexandra; Godtliebsen, Fred; Dalianis, Hercules (Journal article; Tidsskriftartikkel; Peer reviewed, 2020)
      Sensitive data is normally required to develop rule-based or train machine learning-based models for de-identifying electronic health record (EHR) clinical notes; and this presents important problems for patient privacy. In this study, we add non-sensitive public datasets to EHR training data; (i) scientific medical text and (ii) Wikipedia word vectors. The data, all in Swedish, is used to train a ...

    • 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 ...