Many high quality studies have emerged from public databases, such as Surveillance, Epidemiology, and End Results (SEER), National Health and Nutrition Examination Survey (NHANES), The Cancer Genome Atlas (TCGA), and Medical Information Mart for Intensive Care (MIMIC); however, these data are often characterized by a high degree of dimensional heterogeneity, timeliness, scarcity, irregularity, and other characteristics, resulting in the value of these data not being fully utilized. Data-mining technology has been a frontier field in medical research, as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models. Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public databases. This article introduced the main medical public database and described the steps, tasks, and models of data mining in simple language. Additionally, we described data-mining methods along with their practical applications. The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.

Objective: The objective of this study was to identify factors associated with lymph node yield (LNY) during surgeries for pulmonary sarcomatoid carcinoma (PSC) and to determine effects of lymph node density (LND) on the overall survival (OS) of patients with PSC. Materials and Methods: The SEER Research Plus database was searched for data on patients with PSC from 1988 to 2018. Poisson regression was used of all patients with PSC to identify relevant factors associated with LNY. Univariate and multivariate Cox regression analyses were adopted for lymph node (LN)-positive patients to evaluate the impact of LND on OS. The 5-year OS rates of patients with PSC were compared based on their LN status and LND. Results: There were 545 eligible patients in the study sample, 175 of which were LN-positive. These patients had significantly lower 5-year OS than those with no positive LNs (P<0.001). Poisson regression analysis indicated relevant factors increasing LNY included higher diagnosis age, non-Hispanic American Indian or Alaska Native races, larger tumor, pleomorphic carcinoma histology, and more advanced disease stages. The Cox regression analysis indicated higher LND (P=0.022) was probably associated with a worse prognosis for LN-positive patients. The group with LND ≥0.12 had a higher risk of death than the group with LND <0.12 (P<0.001) among LN-positive patients with PSC. Conclusions: Patients with PSC with high LND experienced worse outcomes than those with low LND. Further risk stratification of patients with PSC may help to improve survival benefits based on prognostic indicators of LND.