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Constructing a Clinical Patient Similarity Network of Gastric Cancer

Constructing a Clinical Patient Similarity Network of Gastric Cancer

Authors :
Rukui Zhang
Zhaorui Liu
Chaoyu Zhu
Hui Cai
Kai Yin
Fan Zhong
Lei Liu
Source :
Bioengineering, Vol 11, Iss 8, p 808 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Objectives: Clinical molecular genetic testing and molecular imaging dramatically increase the quantity of clinical data. Combined with the extensive application of electronic health records, a medical data ecosystem is forming, which calls for big-data-based medicine models. We tried to use big data analytics to search for similar patients in a cancer cohort, showing how to apply artificial intelligence (AI) algorithms to clinical data processing to obtain clinically significant results, with the ultimate goal of improving healthcare management. Methods: In order to overcome the weaknesses of most data processing algorithms that rely on expert labeling and annotation, we uniformly adopted one-hot encoding for all types of clinical data, calculating the Euclidean distance to measure patient similarity and subgrouping via an unsupervised learning model. Overall survival (OS) was investigated to assess the clinical validity and clinical relevance of the model. Results: We took gastric cancers (GCs) as an example to build a high-dimensional clinical patient similarity network (cPSN). When performing the survival analysis, we found that Cluster_2 had the longest survival rates, while Cluster_5 had the worst prognosis among all the subgroups. As patients in the same subgroup share some clinical characteristics, the clinical feature analysis found that Cluster_2 harbored more lower distal GCs than upper proximal GCs, shedding light on the debates. Conclusion: Overall, we constructed a cancer-specific cPSN with excellent interpretability and clinical significance, which would recapitulate patient similarity in the real-world. The constructed cPSN model is scalable, generalizable, and performs well for various data types.

Details

Language :
English
ISSN :
23065354
Volume :
11
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Bioengineering
Publication Type :
Academic Journal
Accession number :
edsdoj.2b06ae3b72f54906a4994fc3597ab172
Document Type :
article
Full Text :
https://doi.org/10.3390/bioengineering11080808