What Patients Say: Large-Scale Analyses of Replies to the Parkinson's Disease Patient Report of Problems (PD-PROP).

Abstract

Free-text, verbatim replies in the words of people with Parkinson's disease (PD) have the potential to provide unvarnished information about their feelings and experiences. Challenges of processing such data on a large scale are a barrier to analyzing verbatim data collection in large cohorts. To develop a method for curating responses from the Parkinson's Disease Patient Report of Problems (PD-PROP), open-ended questions that asks people with PD to report their most bothersome problems and associated functional consequences. Human curation, natural language processing, and machine learning were used to develop an algorithm to convert verbatim responses to classified symptoms. Nine curators including clinicians, people with PD, and a non-clinician PD expert classified a sample of responses as reporting each symptom or not. Responses to the PD-PROP were collected within the Fox Insight cohort study. Approximately 3,500 PD-PROP responses were curated by a human team. Subsequently, approximately 1,500 responses were used in the validation phase; median age of respondents was 67 years, 55% were men and median years since PD diagnosis was 3 years. 168,260 verbatim responses were classified by machine. Accuracy of machine classification was 95% on a held-out test set. 65 symptoms were grouped into 14 domains. The most frequently reported symptoms at first report were tremor (by 46% of respondents), gait and balance problems (>39%), and pain/discomfort (33%). A human-in-the-loop method of curation provides both accuracy and efficiency, permitting a clinically useful analysis of large datasets of verbatim reports about the problems that bother PD patients.