Objective: This study aims to develop an end-to-end natural language processing pipeline for triage and diagnosis of COVID-19 from patient-authored social media posts . Materials and

Methods: The text processing pipeline first extracts COVID-19 symptoms and related concepts such as severity, duration, negations, and body parts from patients posts using conditional random fields . An unsupervised rule-based algorithm is then applied to establish relations between concepts in the next step of the pipeline . The extracted concepts and relations are subsequently used to construct two different vector representations of each post . These vectors are applied separately to build support vector machine learning models to triage patients into three categories and diagnose them for COVID-19 .

Results: We report that Macro- and Micro-averaged F_1 scores in the range of 71-96% and 61-87%, respectively, for the triage and diagnosis of COVID-19, when the models are trained on ground truth labelled data . Our experimental results indicate that similar performance can be achieved when the models are trained using predicted labels from concept extraction and rule-based classifiers, thus yielding end-to-end machine learning .

Discussion: We highlight important features uncovered by our diagnostic machine learning models and compare them with the most frequent symptoms revealed in another COVID-19 dataset . In particular, we found that the most important features are not always the most frequent ones .

Conclusions: Our preliminary results show that it is possible to automatically triage and diagnose patients for COVID-19 from natural language narratives using a machine learning pipeline.