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

Author:

Marras Connie1,Arbatti Lakshmi2,Hosamath Abhishek2,Amara Amy3,Anderson Karen E.4,Chahine Lana M.5,Eberly Shirley6,Kinel Dan7,Mantri Sneha8,Mathur Soania9,Oakes David6,Purks Jennifer L.7,Standaert David G.9,Tanner Caroline M.10,Weintraub Daniel11,Shoulson Ira27

Affiliation:

1. Edmond J Safra Program in Parkinson’s Disease, University Health Network, University of Toronto, Toronto, Canada

2. Grey Matter Technologies, a Wholly Owned Subsidiary of Modality.ai, San Francisco, CA, USA

3. Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

4. Departments of Psychiatry and Neurology, Georgetown University, Washington DC, USA

5. Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA

6. Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA

7. Department of Neurology, University of Rochester, Rochester NY, USA

8. Department of Neurology, Duke University, Durham, NC, USA

9. PD Avengers, Toronto, Canada

10. Department of Neurology, Weill Institute for Neurosciences, University of California – San Francisco, San Francisco, CA, USA

11. Departments of Psychiatry and Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

Abstract

Background: 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. Objective: 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. Methods: 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. Results: 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%). Conclusion: 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.

Publisher

IOS Press

Subject

Cellular and Molecular Neuroscience,Neurology (clinical)

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