Toward the Automation of Diagnostic Conversation Analysis in Patients with Memory Complaints
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.
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Diagnostic accuracy decreases as WER increases, but the correlation is relatively weak when using features that are more robust to ASR errors, such as word frequency and word length features.
“Additionally, they were able to confirm that even with their model, diagnostic accuracy decreases with increasing WER, as expected, but the correlation between the two is relatively weak when selecting certain features that are more robust to ASR errors (such as word frequency and word length related features)15.”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [45]
An ASR model trained on a smaller in-domain dataset can achieve better WER-based accuracy than one trained on a larger out-of-domain dataset.
“They were able to show that an ASR model trained with a smaller in-domain dataset could improve WER-based accuracy than one trained with a larger out-of-domain dataset.”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [45]
Another example of speech generated within clinical assessment is the recording of patient-doctor consultations of cognitive examinations.
“Another example of speech generated within clinical assessment is the recording of patient-doctor consultations [8, 85, 86] of cognitive examinations (e.g., MMSE [83]).”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [86]
Very few reviewed papers report use of artificial neural networks, recurrent neural networks, multi-layer perceptron, or convolutional neural networks.
“As regards choice of machine learning methods, very few papers report the use of artificial neural networks [91, 95], recurrent neural networks [82], multi-layer perceptron [44, 67, 79, 80, 86], or convolutional neural networks [60, 85].”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [86]
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Roberts, A., Fegter, O., Orange, J. B., & Rogalski, E. J. (2025) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198888482.013.0022 · Cited by 1 · see citing works
Attas, D., Mirheidari, B., Blackburn, D. J., Venneri, A., Walker, T., Harkness, K., Reuber, M., Blackmore, C., & Christensen, H. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-80129-8_5 · Cited by 1 · see citing works
Self-citationAl-Hameed, S. (2019) · White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York) · Cited by 1 · see citing works
Quality of Life Assessment of Diabetic patients from health-related blogsPreprint
Lenzi, A., Maranghi, M., Stilo, G., & Velardi, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.06548
Deep discourse analysis for early prediction of multi-type dementia
Alkenani, A. H. (2023) · Queensland University of Technology · doi:10.5204/thesis.eprints.240368
AI-powered assessments for mild cognitive impairment detection: A comprehensive review
Vinesh, T., Lavanya, M. S., Arun, V., Juremi, J., Tapkire, M. D., Shashikala, B. M., & Fatin, A. M. S. (2026) · AIP conference proceedings · doi:10.1063/5.0331506
Khanna, Z., Ho, D., Remus, A., Raczkowska, M. N., Montalan, J. R., & Saha, P. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1875702
Neuropsychological assessment in functional cognitive disorder: a scoping review
Mammì, A., Reuber, M., Pascarella, A., Abelardo, D., Cutellè, R., Bulgari, A., Ferlazzo, E., & Gasparini, S. (2026) · Psychiatry Research · doi:10.1016/j.psychres.2026.117508
Self-citation
- Authors
- Mirheidari, B., Blackburn, D., Harkness, K., Walker, T., Venneri, A., Reuber, M., & Christensen, H.
- Affiliations
- University of Sheffield (GB)
- Royal Hallamshire Hospital (GB)
- IRCCS San Camillo Hospital (IT)
- Corresponding author
- Mirheidari, Bahman
- ORCID
- Venue
- Journal of Alzheimer’s Disease
- Publisher
- SAGE Publications
- Published
- 2016-04-15
- Volume
- 58
- Issue
- 2
- Pages
- 373-387
- ISSN
- 1387-2877 (ISSN-L)
- 1875-8908
- OpenAlex
- W2606308239
- Cited by
- 54
- References
- 47
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 5 · 2023: 6 · 2022: 3 · 2021: 9 · 2020: 5 · 2019: 10 · 2018: 8
- FWCI
- 5.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Alzheimer s Disease (publishedVersion)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Social Sciences › Arts and Humanities › Language and Linguistics › Language, Discourse, Communication Strategies
- Keywords
- doctor-patient interaction
- MeSH terms
- Machine Learning, Aged, Automation, Communication, Diagnosis, Differential, Female, Humans, Male, Memory Disorders, Middle Aged, Neuropsychological Tests, Physician-Patient Relations, Retrospective Studies, Neurodegenerative Diseases
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