Neural network analysis of Chinese herbal medicine prescriptions for patients with colorectal cancer

Abstract

Traditional Chinese Medicine (TCM) is an experiential form of medicine with a history dating back thousands of years. The present study aimed to utilize neural network analysis to examine specific prescriptions for colorectal cancer (CRC) in clinical practice to arrive at the most effective prescription strategy. The study analyzed the data of 261 CRC cases recruited from a total of 141,962 cases of renowned veteran TCM doctors collected from datasets of both the DeepMedic software and TCM cancer treatment books. The DeepMedic software was applied to normalize the symptoms/signs and Chinese herbal medicine (CHM) prescriptions using standardized terminologies. Over 20 percent of CRC patients demonstrated symptoms of poor appetite, fatigue, loose stool, and abdominal pain. By analyzing the prescription patterns of CHM, we found that Atractylodes macrocephala (Bai-zhu) and Poria (Fu-ling) were the most commonly prescribed single herbs identified through analysis of medical records, and supported by the neural network analysis; although there was a slight difference in the sequential order. The study revealed an 81.9% degree of similarity of CHM prescriptions between the medical records and the neural network suggestions. The patterns of nourishing Qi and eliminating dampness were the most common goals of clinical prescriptions, which corresponds with treatments of CRC patients in clinical practice. This is the first study to employ machine learning, specifically neural network analytics to support TCM clinical diagnoses and prescriptions. The DeepMedic software may be used to deliver accurate TCM diagnoses and suggest prescriptions to treat CRC.