Research on the Development and Validation of Personalized Exercise Prescription System for Breast Cancer Patients Based on Large Language Models

Research on the Development and Validation of Personalized Exercise Prescription System for Breast Cancer Patients Based on Large Language Models

The goal of this observational study is to develop and evaluate a large language model (LLM)-based decision support system for exercise prescription in breast cancer patients, aiming to provide personalized decision-making support for postoperative breast cancer rehabilitation.

The main questions it aims to answer are:

How accurate, personalized, and safe are the exercise prescriptions generated by the fine-tuned LLM? How does the model's performance compare with other mainstream or non-fine-tuned models across different stages and subtypes of breast cancer? Participants are postoperative breast cancer rehabilitation patients treated at Sun Yat-sen Memorial Hospital of Sun Yat-sen University. They will have demographic, tumor, treatment, and physical fitness data collected; receive personalized exercise prescriptions automatically generated by the LLM-based system; and provide subjective evaluations on the feasibility and executability of the prescriptions.

Inclusion Criteria:

• Adult patients aged 18-75 years with early-stage breast cancer who have undergone surgical treatment, such as mastectomy or breast-conserving surgery.
• The patients had clear clinical diagnosis and complete electronic medical record information (including demographic information, tumor stage and classification, treatment history, physical performance evaluation data, etc.).

Exclusion Criteria:

• Presence of severe postoperative complications or comorbidities (e.g., uncontrolled cardiac or pulmonary disease) that may interfere with participation in rehabilitation or pose a safety risk.