Breast Cancer Chemotherapy Risk Prediction Mathematical Model

Overview

This is a cohort study. This study is to develop a predictive model of the side effects after chemotherapy, exploreing the potential risk factors of the side effects such as myelosuppression and chemotherapy realted vomit after the chemotherapy, so that it could help to alleviate patients' fear and anxiety about the side effects and the toxicity of chemotherapy. The potential risk factors were measured at baseline.

Firstly, the investigator will extact the "macro" information about physical conditon, which comes from syndrome-constitutional differentiation in the light of Traditonal Chinease Medicine (TCM) theory. Then combined with the "micro" information about pathological and biological markers, "TCM clinical phenotype" is extracted by Text Mining, and shows the full picture of the clinical features of breast cancer, which will include most of risk factors related to occurrence of adverse of chemotherapy fo breast cancer patients to establish two corresponding quatitative databases. Furthermore, predictive models will be built according to the predictors selected from an analysis on new methods of econometrics-Granger causality and cointegration analysis between "TCM clinical phenotype" and adverse effects of chemotherapy. The research results will be used to establish effective predictive models to lay the foundation for optimizing individualized treatment program of breast cancer by TCM and Western medicine.

Conditions

Eligibility

Locations (1)

Outcomes

Central Contacts