Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Br J Haematol. 2023 Jul;202(2):219-229. doi: 10.1111/bjh.18861. Epub 2023 May 11.
Machine learning (ML) approaches have been applied in the diagnosis and prediction of haematological malignancies. The consideration of ML algorithms to complement or replace current standard of care approaches requires investigation into the methods used to develop relevant algorithms and understanding the accuracy, sensitivity and specificity of such algorithms in the diagnosis and prognosis of malignancies. Here we discuss methods used to develop ML algorithms and review original research studies for assessing the use of ML algorithms in the diagnosis and prognosis of lymphoma.
机器学习 (ML) 方法已应用于血液系统恶性肿瘤的诊断和预测。考虑使用 ML 算法来补充或替代当前的标准治疗方法，需要研究用于开发相关算法的方法，并了解此类算法在恶性肿瘤诊断和预后中的准确性、敏感性和特异性。在这里，我们讨论了用于开发 ML 算法的方法，并回顾了评估 ML 算法在淋巴瘤诊断和预后中的应用的原始研究。
