Out-of-set association analysis of lung cancer drugs and symptoms based on clinical case data mining

Abstract

BACKGROUND:

There are 1.8 million lung cancer deaths worldwide, accounting for 18% of global cancer deaths, including 710,000 in China, accounting for 23.8% of all cancer deaths in China.

OBJECTIVE:

To explore the out-of-set association rules of lung cancer symptoms and drugs through text mining of traditional Chinese medicine (TCM) treatment of lung cancer, and form medical case analysis to analyze the experience of TCM syndrome differentiation in its treatment.

METHODS:

The medical records of all patients diagnosed with lung cancer in Nanjing Chest Hospital from January to December 2018 were collected, and the out-of-set association analysis was performed using the MedCase v5.2 TCM clinical scientific research auxiliary platform based on the frequent pattern growth enhanced association analysis algorithm.

RESULTS:

In terms of TCM treatment of lung cancer, the clinical symptoms with high correlation included cough, expectoration, chest distress, and white phlegm; and the drugs with high correlation included Pinellia ternata, licorice root, white Atractylodes rhizome, and Radix Ophiopogonis; with the prescriptions based on Erchen and Maimendong decoctions.

CONCLUSION:

This analytical study of the medical cases of TCM treatment for lung cancer was performed using data mining techniques, and the out-of-set association rules between clinical symptoms and drugs were analyzed, including the understanding of lung cancer in TCM. Moreover, the essence of experience in drug use was gathered, providing significant scientific guidance for the clinical treatment of lung cancer.