  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, in order to provide researchers and public health
practitioners with additional information on the symptoms, severity and
prevalence of the disease rather than to provide an actionable decision at the
individual level. 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 F1 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 human
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. Also, 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.
