DeepAI AI Chat
Log In Sign Up

Inferring COVID-19 Biological Pathways from Clinical Phenotypes via Topological Analysis

by   Negin Karisani, et al.

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 steps: 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.


page 1

page 2

page 3

page 4


Fast, Structured Clinical Documentation via Contextual Autocomplete

We present a system that uses a learned autocompletion mechanism to faci...

Applying unsupervised keyphrase methods on concepts extracted from discharge sheets

Clinical notes containing valuable patient information are written by di...

Hybrid Text Feature Modeling for Disease Group Prediction using Unstructured Physician Notes

Existing Clinical Decision Support Systems (CDSSs) largely depend on the...

Optimising Chest X-Rays for Image Analysis by Identifying and Removing Confounding Factors

During the COVID-19 pandemic, the sheer volume of imaging performed in a...