Literature DB >> 33983596

Clinical Databases for Breast Cancer Research.

Ki-Tae Hwang1.   

Abstract

Clinical database is a collection of clinical data related to patients, which can be used for analysis and research. Clinical data can be classified into several categories: patient-related, tumor-related, diagnostics-related, treatment-related, outcome-related, administration-related, and other clinical data. Clinical databases can be classified according to the data types of clinical databases, ranges of institutes, and accessibility to data. The numbers of papers and clinical trials are rapidly increasing. Recently, more than 9000 papers related to breast cancer have been published annually, and more than 7000 papers related to human breast cancer are published annually. The speed of increase is expected to be faster and faster in future. Now, almost 8000 clinical trials are registered world widely. Main research areas of breast cancer can be classified into followings; epidemiology, screening and prevention, diagnosis, treatment, and prognosis. Clinical databases that are available for breast cancer research are also introduced in this chapter. The analysis of big data is expected to be the mainstream of breast cancer research using clinical databases. As the technology of artificial intelligence (AI) is rapidly evolving, the technology of deep learning starts to be applied for breast cancer research. In near future, AI technology is predicted to penetrate deeply the field of breast cancer research.

Entities:  

Keywords:  Artificial intelligence; Big data; Breast cancer; Breast cancer research; Clinical data; Clinical database

Mesh:

Year:  2021        PMID: 33983596     DOI: 10.1007/978-981-32-9620-6_26

Source DB:  PubMed          Journal:  Adv Exp Med Biol        ISSN: 0065-2598            Impact factor:   2.622


  2 in total

1.  BCL2 Regulation according to Molecular Subtype of Breast Cancer by Analysis of The Cancer Genome Atlas Database.

Authors:  Ki-Tae Hwang; Kwangsoo Kim; Ji Hyun Chang; Sohee Oh; Young A Kim; Jong Yoon Lee; Se Hee Jung; In Sil Choi
Journal:  Cancer Res Treat       Date:  2017-07-04       Impact factor: 4.679

2.  Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers.

Authors:  George Hripcsak; Jon D Duke; Nigam H Shah; Christian G Reich; Vojtech Huser; Martijn J Schuemie; Marc A Suchard; Rae Woong Park; Ian Chi Kei Wong; Peter R Rijnbeek; Johan van der Lei; Nicole Pratt; G Niklas Norén; Yu-Chuan Li; Paul E Stang; David Madigan; Patrick B Ryan
Journal:  Stud Health Technol Inform       Date:  2015
  2 in total

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