Use of machine learning algorithms to construct models of symptom burden cluster risk in breast cancer patients undergoing chemotherapy.

Journal: Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer

PMID: 39945884

Abstract

PURPOSE: To develop models using different machine learning algorithms to predict high-risk symptom burden clusters in breast cancer patients undergoing chemotherapy, and to determine an optimal model.

Authors

Qingmei Huang

School of Nursing, Fudan University, Shanghai, China.
Yang Yang

Department of Gastrointestinal Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China.
Changrong Yuan

Fudan University, Shanghai, China.
Wen Zhang

Oil Crops Research Institute, Chinese Academy of Agricultural Sciences Wuhan 430062 China peiwuli@oilcrops.cn zhangqi521x@126.com +86-27-8681-2943 +86-27-8671-1839.
Xuqian Zong

School of Nursing, Fudan University, Shanghai, China.
Fulei Wu

School of Nursing, Fudan University, Shanghai, China. wufulei@fudan.edu.cn.

Keywords

Adult Aged Algorithms Antineoplastic Agents Breast Neoplasms Decision Trees Female Humans Logistic Models Machine Learning Middle Aged Neural Networks, Computer Predictive Value of Tests ROC Curve Sensitivity and Specificity Support Vector Machine Symptom Burden

External Resources

View on PubMed Access via DOI PubMed (39945884)

Use of machine learning algorithms to construct models of symptom burden cluster risk in breast cancer patients undergoing chemotherapy.

Abstract

Authors

Keywords

External Resources

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