Predicting Symptom Trajectories After Thoracoscopic Lung Cancer Surgery Using an Interpretable Machine Learning Model

Patients suffer from a variety of symptoms after thoracoscopic surgery. However, there is a lack of validated predictive tools to identify potentially high-risk patients. This study is anticipated to include approximately 1,500 lung cancer patients who undergo thoracoscopic surgery. Latent class mixed modeling (LCMM) will be used to dentify subgroups of patients with similar symptom trajectories. Machine learning models were developed to predict postoperative symptom trajectories based on collected information. Effective prediction of postoperative symptoms can help identify high-risk patients and take preventive measures.

Sex

All

Minimum age

18 Years

Maximum age

80 Years

Inclusion Criteria:

* Age 18-80 years old;
* Pathologically diagnosed lung cancer;
* Undergo thoracoscopic surgery, including video-assisted thoracoscopy and robotic-assisted thoracoscopic surgery;
* no prior history of malignancy or lung surgery;
* Have the ability to complete the scale.

Exclusion Criteria:

* Converted to thoracotomy during thoracoscopic surgery;
* Unable to complete the postoperative scale at least two times;
* Missing data values exceeding 30 percent.