Inferring COVID-19 Biological Pathways from Clinical Phenotypes Via Topological Analysis
5th International Workshop on Health Intelligence, W3PHAI 2021 held in conjection with 35th AAAI Conference on Artificial Intelligence, AAAI 2021
; 1013:147-163, 2022.
Article
in English
| Scopus | ID: covidwho-1777639
ABSTRACT
COVID-19 has caused thousands of deaths around the world and also resulted in a large international economic disruption. Identifying the pathways associated with this illness can help medical researchers to better understand the properties of the condition. This process can be carried out by analyzing the medical records. It is crucial to develop tools and models that can aid researchers with this process in a timely manner. However, medical records are often unstructured clinical notes, and this poses significant challenges to developing the automated systems. In this article, we propose a pipeline to aid practitioners in analyzing clinical notes and revealing the pathways associated with this disease. Our pipeline relies on topological properties and consists of three phases (1) pre-processing the clinical notes to extract the salient concepts, (2) constructing a feature space of the patients to characterize the extracted concepts, and finally, (3) leveraging the topological properties to distill the available knowledge and visualize the result. Our experiments on a publicly available dataset of COVID-19 clinical notes testify that our pipeline can indeed extract meaningful pathways. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
5th International Workshop on Health Intelligence, W3PHAI 2021 held in conjection with 35th AAAI Conference on Artificial Intelligence, AAAI 2021
Year:
2022
Document Type:
Article
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