Local Causal Structure Learning and its Discovery Between Type 2 Diabetes and Bone Mineral Density

TL;DR

This paper introduces PKCL, a novel causal discovery algorithm that leverages prior medical knowledge to efficiently identify causal factors between bone mineral density and Type 2 diabetes from clinical data, reducing the need for extensive experiments.

Contribution

The paper presents PKCL, a new algorithm that integrates prior knowledge into local causal structure learning, improving reliability and efficiency in medical causal discovery.

Findings

PKCL aligns well with existing medical knowledge.

Using prior knowledge enhances causal discovery accuracy.

PKCL outperforms methods without prior knowledge.

Abstract

Type 2 diabetes (T2DM), one of the most prevalent chronic diseases, affects the glucose metabolism of the human body, which decreases the quantity of life and brings a heavy burden on social medical care. Patients with T2DM are more likely to suffer bone fragility fracture as diabetes affects bone mineral density (BMD). However, the discovery of the determinant factors of BMD in a medical way is expensive and time-consuming. In this paper, we propose a novel algorithm, Prior-Knowledge-driven local Causal structure Learning (PKCL), to discover the underlying causal mechanism between BMD and its factors from the clinical data. Since there exist limited data but redundant prior knowledge for medicine, PKCL adequately utilize the prior knowledge to mine the local causal structure for the target relationship. Combining the medical prior knowledge with the discovered causal relationships, PKCL can achieve more reliable results without long-standing medical statistical experiments. Extensive experiments are conducted on a newly provided clinical data set. The experimental study of PKCL on the data is proved to highly corresponding with existing medical knowledge, which demonstrates the superiority and effectiveness of PKCL. To illustrate the importance of prior knowledge, the result of the algorithm without prior knowledge is also investigated.