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Sökning: WFRF:(Chomutare Taridzo) > Challenges and oppo...

  • Tayefi, MaryamNorwegian Centre for E-health Research, Tromsø, Norway (författare)

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

  • Artikel/kapitelEngelska2021

Förlag, utgivningsår, omfång ...

  • 2021-02-14
  • Wiley,2021
  • printrdacarrier

Nummerbeteckningar

  • LIBRIS-ID:oai:DiVA.org:su-192564
  • https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-192564URI
  • https://doi.org/10.1002/wics.1549DOI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:ref swepub-contenttype
  • Ämneskategori:for swepub-publicationtype

Anmärkningar

  • 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 sources of information. Advanced statistical algorithms in natural language processing, machine learning, deep learning, and radiomics have increasingly been used for analyzing clinical text and images. Although there exist many challenges that have not been fully addressed, which can hinder the use of unstructured data, there are clear opportunities for well-designed diagnosis and decision support tools that efficiently incorporate both structured and unstructured data for extracting useful information and provide better outcomes. However, access to clinical data is still very restricted due to data sensitivity and ethical issues. Data quality is also an important challenge in which methods for improving data completeness, conformity and plausibility are needed. Further, generalizing and explaining the result of machine learning models are important problems for healthcare, and these are open challenges. A possible solution to improve data quality and accessibility of unstructured data is developing machine learning methods that can generate clinically relevant synthetic data, and accelerating further research on privacy preserving techniques such as deidentification and pseudonymization of clinical text.

Ämnesord och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Ngo, PhuongNorwegian Centre for E-health Research, Tromsø, Norway (författare)
  • Chomutare, TaridzoNorwegian Centre for E-health Research, Tromsø, Norway (författare)
  • Dalianis, HerculesStockholms universitet,Institutionen för data- och systemvetenskap,Norwegian Centre for E-health Research, Tromsø, Norway(Swepub:su)hercules (författare)
  • Salvi, ElisaNorwegian Centre for E-health Research, Tromsø, Norway (författare)
  • Budrionis, AndriusNorwegian Centre for E-health Research, Tromsø, Norway (författare)
  • Godtliebsen, FredNorwegian Centre for E-health Research, Tromsø, Norway; UiT The Arctic University of Norway, Tromsø, Norway (författare)
  • Norwegian Centre for E-health Research, Tromsø, NorwayInstitutionen för data- och systemvetenskap (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:Wiley Interdisciplinary Reviews: Wiley13:61939-51081939-0068

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