Quantitative patient graph analysis for transient ischemic attack risk factor distribution based on electronic medical records

Jian Wen; Tianmei Zhang; Shangrong Ye; Peng Zhang; Ruobing Han; Xiaowang Chen; Ran Huang; Anjun Chen; Qinghua Li

doi:10.1016/j.heliyon.2023.e22766

Quantitative patient graph analysis for transient ischemic attack risk factor distribution based on electronic medical records

Heliyon. 2023 Nov 25;10(1):e22766. doi: 10.1016/j.heliyon.2023.e22766. eCollection 2024 Jan 15.

Authors

Jian Wen¹, Tianmei Zhang¹, Shangrong Ye¹, Peng Zhang¹, Ruobing Han¹, Xiaowang Chen¹, Ran Huang², Anjun Chen³, Qinghua Li¹

Affiliations

¹ Guilin Medical University Affiliated Hospital, Guilin, Guangxi, China.
² West China Hospital, Chengdu, Sichuan, China.
³ Learning Health Community, Palo Alto, CA, USA.

Abstract

A transient ischemic attack (TIA) affects millions of people worldwide. Although TIA risk factors have been identified individually, a systemic quantitative analysis of all health factors relevant to TIA using electronic medical records (EMR) remains lacking. This study employed a data-driven approach, leveraging hospital EMR data to create a TIA patient health factor graph. This graph consisted of 737 TIA and 737 control patient nodes, 740 health factor nodes, and over 33,000 relations between patients and factors. For all health factors in the graph, the connection delta ratios (CDRs) were determined and ranked, generating a quantitative distribution of TIA health factors. A literature review confirmed 56 risk factors in the distribution and unveiled a potential new risk factor "rhinosinusitis" for future validation. Moreover, the patient graph was visualized together with the TIA knowledge graph in the Unified Medical Language System. This integration enables clinicians to access and visualize patient data and international standard knowledge within a unified graph. In conclusion, graph CDR analysis can effectively quantify the distribution of TIA risk factors. The resulting TIA risk factor distribution might be instrumental in developing new risk prediction machine learning models for screening and early detection of TIA.

Keywords: Connection delta ratio; Electronic medical records; Knowledge graph; Patient graph; Risk factor; Transient ischemic attack; UMLS.