scholarly journals CATTLE (CAncer treatment treasury with linked evidence): An integrated knowledge base for personalized oncology research and practice

2017 ◽  
Vol 6 (3) ◽  
pp. 188-196 ◽  
Author(s):  
E Soysal ◽  
H-J Lee ◽  
Y Zhang ◽  
L-C Huang ◽  
X Chen ◽  
...  
Keyword(s):  
Cancer Treatment ◽  
Knowledge Base ◽  
Research And Practice ◽  
Oncology Research ◽  
Personalized Oncology ◽  
Linked Evidence ◽  
Integrated Knowledge

Conclusion

2020 ◽  
pp. 243-247
Author(s):  
James Pickett
Keyword(s):  
Knowledge Base ◽  
Social Group ◽  
Islamic Law ◽  
Social Roles ◽  
Social Groups ◽  
Social Functions ◽  
Regional Networks ◽  
Mix And Match ◽  
Forms Of Knowledge ◽  
Integrated Knowledge

This concluding chapter explains that for all of their eclecticism, and for all their seeming paradoxes, the polymaths of Islam were united by a common madrasa education, mastery of a canon of texts, and shared regional networks. Their curriculum went far beyond the grammar and logic emphasized in the madrasa. Even mastering substantive Islamic law from medieval Arabic texts was necessary, but not sufficient, to distinguish a high Persianate intellectual from his many, many competitors. Most of the ulama — especially those who rose to the top — studied a plethora of collateral disciplines: poetry, mysticism, astronomy, calligraphy, medicine, trade, and more. Secondary scholarship often pairs these forms of knowledge with discrete communities, differentiating scholars, poets, sufis, and physicians into distinct social groups, with the sufi-ulama dichotomy especially pronounced. However, these were not separate groups with separate corporate identities. Rather, they were discrete social roles performed by a single social group. Their integrated knowledge base allowed them to mix and match social functions with impunity.

scholarly journals Machine Learning in Oncology: What Should Clinicians Know?

2020 ◽  
pp. 799-810
Author(s):  
Matthew Nagy ◽  
Nathan Radakovich ◽  
Aziz Nazha
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Data ◽  
Cancer Diagnosis ◽  
Health Data ◽  
Computer Processing ◽  
Research And Practice ◽  
New Methods ◽  
Oncology Research ◽  
Processing Power

The volume and complexity of scientific and clinical data in oncology have grown markedly over recent years, including but not limited to the realms of electronic health data, radiographic and histologic data, and genomics. This growth holds promise for a deeper understanding of malignancy and, accordingly, more personalized and effective oncologic care. Such goals require, however, the development of new methods to fully make use of the wealth of available data. Improvements in computer processing power and algorithm development have positioned machine learning, a branch of artificial intelligence, to play a prominent role in oncology research and practice. This review provides an overview of the basics of machine learning and highlights current progress and challenges in applying this technology to cancer diagnosis, prognosis, and treatment recommendations, including a discussion of current takeaways for clinicians.

Presentation design using an integrated knowledge base

1991 ◽  
pp. 241-258 ◽  
Author(s):  
Yigal Arens ◽  
Lawrence Miller ◽  
Norman Sondheimer
Keyword(s):  
Knowledge Base ◽  
Integrated Knowledge
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