  The USA Food and Drug Administration has accorded increasing importance to
patient-reported problems in clinical and research settings. In this paper, we
explore one of the largest online datasets comprising 170,141 open-ended
self-reported responses (called "verbatims") from patients with Parkinson's
(PwPs) to questions about what bothers them about their Parkinson's Disease and
how it affects their daily functioning, also known as the Parkinson's Disease
Patient Report of Problems. Classifying such verbatims into multiple clinically
relevant symptom categories is an important problem and requires multiple steps
- expert curation, a multi-label text classification (MLTC) approach and large
amounts of labelled training data. Further, human annotation of such large
datasets is tedious and expensive. We present a novel solution to this problem
where we build a baseline dataset using 2,341 (of the 170,141) verbatims
annotated by nine curators including clinical experts and PwPs. We develop a
rules based linguistic-dictionary using NLP techniques and graph database-based
expert phrase-query system to scale the annotation to the remaining cohort
generating the machine annotated dataset, and finally build a Keras-Tensorflow
based MLTC model for both datasets. The machine annotated model significantly
outperforms the baseline model with a F1-score of 95% across 65 symptom
categories on a held-out test set.
