Pattern Identification and Acupuncture Prescriptions Based on Real-World Data Using Artificial Intelligence

Abstract

In traditional East Asian medicine, acupuncture practitioners gather clinical data, identify patterns, and choose appropriate acupoints. The pattern identification procedure is crucial for clinical diagnosis and acupuncture treatment. Understanding the pattern identification process, i.e. gathering and synthesizing clinical information from patient signs and symptoms, is crucial for characterizing the complicated relationships between symptoms and acupoints. Here, we briefly overview recent studies describing the use of a bodily sensation map to identify spatial patterns of “acupoint indications”, an artificial neural network model to characterize the rules connecting symptoms with acupoints, and medical data extrapolated from case reports to reveal associations between diagnoses and acupoint prescriptions. We also propose a method based on pattern identification to optimize acupoint selection for treatment. Artificial intelligence has substantially advanced traditional East Asian medicine by facilitating decision-making and aids understanding of clinical decision-making as it relates to acupuncture treatment.

Keywords:

• Acupuncture
• artificial intelligence
• data mining
• pattern identification
• real world data

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Data Availability

The authors can provide this upon reasonable request.

Additional information

Funding

This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HF22C0023) and an Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korea government (MSIT) [RS-2022-00155911, Artificial Intelligence Convergence Innovation Human Resources Development (Kyung Hee University)].

Notes on contributors

Ye-Seul Lee

Ye-Seul Lee, KMD, MPH, PhD, is a Senior Researcher in Jaseng Spine and Joint Research Institute, Jaseng Medical Foundation. She earned her masters degree in Public Health in Seoul National University, and her PhD in Korean Medicine, Kyung Hee University. Her work experiences include the managing editor of ICD-11 TM Chapter at the World Health Organization (WHO) and assistant professor at College of Korean Medicine, Gachon University. Her work focuses on data-driven approach and health informatics in acupuncture research.

Younbyoung Chae

Younbyoung Chae works at College of Korean Medicine, Kyung Hee University, Seoul, Korea. In 2008, he earned a Ph.D. in Korean medicine from Kyung Hee University, and in 2015, he earned a second Ph.D. in brain cognitive engineering from Korea University. His areas of interest in study are acupuncture and neuroimaging, especially as it relates to emotion and pain. He has developed bodily sensation map tool to find association between diseases and acupoints. He also published research papers on the use of acupoints in real world clinical settings.