Toward the Automation of Diagnostic Conversation Analysis in Patients with Memory Complaints

Bahman Mirheidari, Daniel Blackburn, Kirsty Harkness, Traci Walker, Annalena Venneri, Markus Reuber, Heidi Christensen

Research output: Contribution to journalArticle

Abstract

Background: The early diagnosis of dementia is of great clinical and social importance. A recent study using the qualitative methodology of conversation analysis (CA) demonstrated that language and communication problems are evident during interactions between patients and neurologists, and that interactional observations can be used to differentiate between cognitive difficulties due to neurodegenerative disorders (ND) or functional memory disorders (FMD). Objective: This study explores whether the differential diagnostic analysis of doctor-patient interactions in a memory clinic can be automated. Methods: Verbatim transcripts of conversations between neurologists and patients initially presenting with memory problems to a specialist clinic were produced manually (15 with FMD, and 15 with ND). A range of automatically detectable features focusing on acoustic, lexical, semantic, and visual information contained in the transcripts were defined aiming to replicate the diagnostic qualitative observations. The features were used to train a set of five machine learning classifiers to distinguish between ND and FMD. Results: The mean rate of correct classification between ND and FMD was 93 ranging from 97 by the Perceptron classifier to 90 by the Random Forest classifier.Using only the ten best features, the mean correct classification score increased to 95. Conclusion: This pilot study provides proof-of-principle that a machine learning approach to analyzing transcripts of interactions between neurologists and patients describing memory problems can distinguish people with neurodegenerative dementia from people with FMD.

Original languageEnglish
Pages (from-to)373-387
Number of pages15
JournalJournal of Alzheimer's Disease
Volume58
Issue number2
DOIs
Publication statusPublished - Jan 1 2017

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Keywords

  • analysis
  • dementia
  • language
  • machine learning
  • speech recognition software

ASJC Scopus subject areas

  • Clinical Psychology
  • Geriatrics and Gerontology
  • Psychiatry and Mental health

Cite this

Mirheidari, B., Blackburn, D., Harkness, K., Walker, T., Venneri, A., Reuber, M., & Christensen, H. (2017). Toward the Automation of Diagnostic Conversation Analysis in Patients with Memory Complaints. Journal of Alzheimer's Disease, 58(2), 373-387. https://doi.org/10.3233/JAD-160507