A Novel Framework for Understanding the Pattern Identification of Traditional Asian Medicine From the Machine Learning Perspective.

Hyojin Bae,Sanghun Lee,Choong-Yeol Lee,Chang-Eop Kim

doi:10.3389/fmed.2021.763533

Abstract

Pattern identification (PI), a unique diagnostic system of traditional Asian medicine, is the process of inferring the pathological nature or location of lesions based on observed symptoms. Despite its critical role in theory and practice, the information processing principles underlying PI systems are generally unclear. We present a novel framework for comprehending the PI system from a machine learning perspective. After a brief introduction to the dimensionality of the data, we propose that the PI system can be modeled as a dimensionality reduction process and discuss analytical issues that can be addressed using our framework. Our framework promotes a new approach in understanding the underlying mechanisms of the PI process with strong mathematical tools, thereby enriching the explanatory theories of traditional Asian medicine.

Highlights

Pattern identification (PI), a distinctive diagnostic system found in traditional Asian medicine (TAM), is a clinical reasoning process that uses the signs and symptoms of patients to identify diagnostic patterns [1]
Inspired by the idea that machine learning (ML) models can help capture critical aspects of the brain’s computation, we present a novel framework for explaining how information is processed in the PI system and why it is effective
The PI system can be modeled as the process of representing high-dimensional symptom data in a low-dimensional space defined by a few latent patterns

Summary

INTRODUCTION

Pattern identification (PI), a distinctive diagnostic system found in traditional Asian medicine (TAM), is a clinical reasoning process that uses the signs and symptoms of patients to identify diagnostic patterns [1]. It can be said that PI is a strategy chosen to make diagnostic decisions based on naked sense observations and to determine corresponding treatments Despite their centrality in theory and practice, the information processing principles of PI have remained relatively superficial. Approaches based on machine learning (ML) have demonstrated remarkable performance in a variety of tasks, including image classification, speech processing, and natural language processing, all of which are difficult to solve using knowledge-based approaches [7] This success has spawned approaches in systems neuroscience that use ML to study how the brain works [8–11]. By leveraging ML’s framework, we can adopt powerful mathematical tools, broaden the scope of inquiry, and enrich explanatory theory in TAM

A Brief Introduction to Dimensionality Reduction

DISCUSSION

DATA AVAILABILITY STATEMENT