  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.
