Skip to main content

Classification of Fall Types in Parkinson's Disease from Self-report Data Using Natural Language Processing

  • Conference paper
  • First Online:
Artificial Intelligence in Medicine (AIME 2023)

Abstract

Falls are a leading cause of injury globally, and people with Parkinson’s disease are particularly at risk. An important step in reducing the probability of falls is to identify their causes, but manually classifying fall types is laborious and requires expertise. Natural language processing (NLP) approaches hold potential to automate fall type identification from descriptions. The aim of this study was to develop and evaluate NLP–based methods to classify fall types from Parkinson’s disease patient self-report data. We trained supervised NLP classifiers using an existing dataset consisting of both structured and unstructured data, including the age, gender, and duration of Parkinson's disease of the faller, as well as the fall location, free-text fall description, and fall class of each fall. We trained supervised classification models to predict fall class based on these attributes, and then performed an ablation study to determine the most important factors influencing the model. The best performing classifier was a hard voting ensemble model that combined the Adaboost, unweighted decision tree, weighted k-nearest neighbor, naïve Bayes, random forest, and support vector machine classifiers. On the testing set, this ensemble classifier achieved an F1-macro of 0.89. We also experimented with a transformer-based model, but its performance was subpar compared to that of the other models. Our study demonstrated that automatic fall type classification in Parkinson's disease patients is possible via NLP and supervised classification.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Bloem, B.R., Grimbergen, Y.A.M., Cramer, M., Willemsen, M., Zwinderman, A.H.: Prospective assessment of falls in Parkinson’s disease. J. Neurol. 248, 950–958 (2001). https://doi.org/10.1007/s004150170047

    Article  Google Scholar 

  2. Maki, B.E., Holliday, P.J., Topper, A.K.: A prospective study of postural balance and risk of falling in an ambulatory and independent elderly population. J. Gerontol. 49, M72–M84 (1994). https://doi.org/10.1093/geronj/49.2.M72

    Article  Google Scholar 

  3. Burns, E., Kakara, R.: Deaths from Falls Among Persons Aged ≥65 Years — United States, 2007–2016. MMWR Morb. Mortal. Wkly. Rep. 67, 509–514 (2018). https://doi.org/10.15585/mmwr.mm6718a1

  4. Florence, C.S., Bergen, G., Atherly, A., Burns, E., Stevens, J., Drake, C.: Medical costs of fatal and nonfatal falls in older adults: medical costs of falls. J. Am. Geriatr. Soc. 66, 693–698 (2018). https://doi.org/10.1111/jgs.15304

    Article  Google Scholar 

  5. Haddad, Y.K., Bergen, G., Florence, C.S.: Estimating the economic burden related to older adult falls by state. J. Public Health Manag. Pract. 25, E17–E24 (2019). https://doi.org/10.1097/PHH.0000000000000816

    Article  Google Scholar 

  6. Stack, E., Ashburn, A.: Fall events described by people with Parkinson’s disease: implications for clinical interviewing and the research agenda. Physiother. Res. Int. 4, 190–200 (1999). https://doi.org/10.1002/pri.165

    Article  Google Scholar 

  7. Ross, A., Yarnall, A.J., Rochester, L., Lord, S.: A novel approach to falls classification in Parkinson’s disease: development of the Fall-Related Activity Classification (FRAC). Physiotherapy 103, 459–464 (2017). https://doi.org/10.1016/j.physio.2016.08.002

    Article  Google Scholar 

  8. Ashburn, A., Stack, E., Ballinger, C., Fazakarley, L., Fitton, C.: The circumstances of falls among people with Parkinson’s disease and the use of Falls Diaries to facilitate reporting. Disabil. Rehabil. 30, 1205–1212 (2008). https://doi.org/10.1080/09638280701828930

    Article  Google Scholar 

  9. Pelicioni, P.H.S., Menant, J.C., Latt, M.D., Lord, S.R.: Falls in Parkinson’s disease subtypes: risk factors, locations and circumstances. Int. J. Environ. Res. Public. Health. 16, 2216 (2019). https://doi.org/10.3390/ijerph16122216

    Article  Google Scholar 

  10. Magnani, P.E., et al.: Use of the BESTest and the Mini-BESTest for fall risk prediction in community-dwelling older adults between 60 and 102 years of age. J. Geriatr. Phys. Ther. 43, 179–184 (2020). https://doi.org/10.1519/JPT.0000000000000236

    Article  Google Scholar 

  11. Houssein, E.H., Mohamed, R.E., Ali, A.A.: Machine learning techniques for biomedical natural language processing: a comprehensive review. IEEE Access. 9, 140628–140653 (2021). https://doi.org/10.1109/ACCESS.2021.3119621

    Article  Google Scholar 

  12. Tohira, H., Finn, J., Ball, S., Brink, D., Buzzacott, P.: Machine learning and natural language processing to identify falls in electronic patient care records from ambulance attendances. Inform. Health Soc. Care. 47, 403–413 (2022). https://doi.org/10.1080/17538157.2021.2019038

    Article  Google Scholar 

  13. Guetterman, T.C., Chang, T., DeJonckheere, M., Basu, T., Scruggs, E., Vydiswaran, V.V.: Augmenting qualitative text analysis with natural language processing: methodological study. J. Med. Internet Res. 20, e231 (2018). https://doi.org/10.2196/jmir.9702

    Article  Google Scholar 

  14. Pérez-Toro, P.A., Vásquez-Correa, J.C., Strauss, M., Orozco-Arroyave, J.R., Nöth, E.: Natural language analysis to detect Parkinson’s disease. In: Ekštein, K. (ed.) Text, Speech, and Dialogue, pp. 82–90. Springer, Cham (2019). doi.https://doi.org/10.1007/978-3-030-27947-9_7.

  15. Dorsey, E.R., et al.: Global, regional, and national burden of Parkinson’s disease, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 17, 939–953 (2018). https://doi.org/10.1016/S1474-4422(18)30295-3

  16. McKay, J.L., Lang, K.C., Bong, S.M., Hackney, M.E., Factor, S.A., Ting, L.H.: Abnormal center of mass feedback responses during balance: a potential biomarker of falls in Parkinson’s disease. PLoS ONE 16, e0252119 (2021). https://doi.org/10.1371/journal.pone.0252119

    Article  Google Scholar 

  17. Porter, M.F.: An algorithm for suffix stripping. Program 14, 130–137 (1980). https://doi.org/10.1108/eb046814

    Article  Google Scholar 

  18. Owoputi, O., O’Connor, B., Dyer, C., Gimpel, K., Schneider, N., Smith, N.A.: Improved part-of-speech tagging for online conversational text with word clusters. Presented at the Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies June (2013)

    Google Scholar 

  19. Liu, Y., et al.: RoBERTa: A Robustly Optimized BERT Pretraining Approach

    Google Scholar 

  20. Harris, D.M., et al.: Development of a Parkinson’s disease specific falls questionnaire. BMC Geriatr. 21, 614 (2021). https://doi.org/10.1186/s12877-021-02555-6

    Article  Google Scholar 

  21. Allen, N.E., et al.: Interventions for preventing falls in Parkinson’s disease. Cochrane Database Syst. Rev. 2022 (2022). https://doi.org/10.1002/14651858.CD011574.pub2

  22. Duckham, R.L., Procter-Gray, E., Hannan, M.T., Leveille, S.G., Lipsitz, L.A., Li, W.: Sex differences in circumstances and consequences of outdoor and indoor falls in older adults in the MOBILIZE Boston cohort study. BMC Geriatr. 13, 133 (2013). https://doi.org/10.1186/1471-2318-13-133

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jeanne M. Powell .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Powell, J.M., Guo, Y., Sarker, A., McKay, J.L. (2023). Classification of Fall Types in Parkinson's Disease from Self-report Data Using Natural Language Processing. In: Juarez, J.M., Marcos, M., Stiglic, G., Tucker, A. (eds) Artificial Intelligence in Medicine. AIME 2023. Lecture Notes in Computer Science(), vol 13897. Springer, Cham. https://doi.org/10.1007/978-3-031-34344-5_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-34344-5_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34343-8

  • Online ISBN: 978-3-031-34344-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics