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From Patient Engagement to Precision Oncology: Leveraging Informatics to Advance Cancer Care.
Griffin, Ashley C; Topaloglu, Umit; Davis, Sean; Chung, Arlene E.
Afiliação
  • Griffin AC; University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
  • Topaloglu U; Wake Forest University School of Medicine, Winston-Salem, NC, USA.
  • Davis S; National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
  • Chung AE; University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Yearb Med Inform ; 29(1): 235-242, 2020 Aug.
Article em En | MEDLINE | ID: mdl-32823322
ABSTRACT

OBJECTIVES:

Conduct a survey of the literature for advancements in cancer informatics over the last three years in three specific areas where there has been unprecedented growth 1) digital health; 2) machine learning; and 3) precision oncology. We also highlight the ethical implications and future opportunities within each area.

METHODS:

A search was conducted over a three-year period in two electronic databases (PubMed, Google Scholar) to identify peer-reviewed articles and conference proceedings. Search terms included variations of the following neoplasms[MeSH], informatics[MeSH], cancer, oncology, clinical cancer informatics, medical cancer informatics. The search returned too many articles for practical review (23,994 from PubMed and 23,100 from Google Scholar). Thus, we conducted searches of key PubMed-indexed informatics journals and proceedings. We further limited our search to manuscripts that demonstrated a clear focus on clinical or translational cancer informatics. Manuscripts were then selected based on their methodological rigor, scientific impact, innovation, and contribution towards cancer informatics as a field or on their impact on cancer care and research.

RESULTS:

Key developments and opportunities in cancer informatics research in the areas of digital health, machine learning, and precision oncology were summarized.

CONCLUSION:

While there are numerous innovations in the field of cancer informatics to advance prevention and clinical care, considerable challenges remain related to data sharing and privacy, digital accessibility, and algorithm biases and interpretation. The implementation and application of these findings in cancer care necessitates further consideration and research.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Informática Médica / Medicina de Precisão / Aprendizado de Máquina / Oncologia / Neoplasias Tipo de estudo: Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Informática Médica / Medicina de Precisão / Aprendizado de Máquina / Oncologia / Neoplasias Tipo de estudo: Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article