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Updated: May 13, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

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使用机器学习模型优化复发风险/Prosigna测试指南:瑞典多中心研究

Una Kjällquist1, Nikos Tsiknakis2, Balazs Acs1

  • 1Department of Oncology/Pathology, Karolinska Institutet, Stockholm, Sweden; Theme Cancer, Karolinska University Hospital, Stockholm, Sweden.

Breast (Edinburgh, Scotland)
|May 10, 2025
PubMed
概括

机器学习改善了对激素受体阳性,HER2阴性乳腺癌的基因表达概况的患者选择. 这种方法提高了风险分层的准确性,并减少了对复发风险 (ROR) /Prosigna测试的不必要测试.

关键词:
一个辅助的辅助.乳腺癌 乳腺癌 乳腺癌机器学习是机器学习.普罗西尼加 (Prosigna) 是一个复发的风险 复发的风险

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