Online Textual Symptomatic Assessment Chatbot Based on Q&A Weighted Scoring for Female Breast Cancer Prescreening

Abstract

The increasing number of female breast cancer (FBC) incidences in the East predominated by Chinese language speakers has generated concerns over women’s medicare. To minimize the mortality rate associated with FBC in the region, governments and health experts are jointly encouraging women to undergo mammography screening at the earliest suspicion of FBC symptoms. However, studies show that a huge number of women affected by FBC tend to delay medical consultation at its early stage as a result of factors such as complacency due to unawareness of FBC symptoms, procrastination due to lifestyle, and the feeling of embarrassment in discussing private matters especially with medical personnel of the opposite gender. To address these issues, we propose a symptomatic assessment chatbot (SAC) based on artificial intelligence (AI) designed to prescreen women for FBC symptoms via a textual question-and-answer (Q&A) approach. The purpose of our chatbot is to assist women in engaging in communication regarding FBC symptoms, so as to subsequently initiate formal medical consultations for early FBC diagnosis and treatment. We implemented the SAC systematically with some of the latest natural language processing (NLP) techniques suitable for Chinese word segmentation (CWS) and trained the model with real-world FBC Q&A data obtained from a major hospital in Taiwan. The results from our experiments showed that the SAC achieved very high accuracy in FBC assessment scoring in comparison to FBC patients’ screening benchmark scores obtained from doctors.

Keywords

• female breast cancer (FBC)
• chatbot
• patient-centric healthcare
• NLP
• trigram
• CWS