Symptom-based phenotype discovery in motor neuron disease using natural language processing of electronic health records

Overview

1. Department of Biostatistics and Health Informatics, King’s College London, UK
2. Institute of Health Informatics, University College London, UK
3. National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
4. Department of Basic and Clinical Neuroscience, King’s College London, UK
5. LifeArc, Stevenage, UK
6. School of Health & Wellbeing, University of Glasgow, UK
7. Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, UK
8. Perron Institute for Neurological and Translational Science, Perth, Australia

Abstract

Background: Motor neuron disease (MND) is a fatal neurodegenerative condition with significant clinical heterogeneity that is incompletely captured by existing phenotype classifications based on onset site. Electronic health records (EHRs) contain detailed symptom documentation in clinical narratives that may enable data-driven discovery of clinically meaningful patient subgroups.

Methods: We developed a natural language processing (NLP) pipeline using MedCAT to extract symptoms from clinical notes of 2,361 people with a confirmed diagnosis of MND at a tertiary neurology center. MND cohort confirmation used three complementary methods: clinic attendance records, text-based diagnosis detection, and NLP extraction with negation detection. Extracted symptoms were filtered to Unified Medical Language System semantic type T184 (Sign or Symptom) with removal of negated concepts. Patients were clustered using latent class analysis on binary symptom profiles. Survival differences were assessed using Kaplan-Meier analysis, log-rank tests, and Cox proportional hazards regression.

Results: From the first clinical notes, we identified four clusters of symptoms among 872 patients and 76 symptoms: Motor-Bulbar (n=373), Motor-Tremor (n=154), Sensory-Pain (n=222), and Motor-Respiratory (n=123). When extended to all clinical notes (n=2,065; 184 symptoms), these reorganized into three clusters: Autonomic-Respiratory (n=472), Nocturnal-Respiratory (n=338), and Classic Motor (n=1,255). Survival differences were significant across all clusters in both the first notes and all notes analyses (log-rank p < 0.001).

Conclusions: NLP-based symptom extraction from EHRs identifies clinically meaningful MND subgroups that extend beyond traditional onset-site classifications. Autonomic-respiratory symptom burden is associated with poorer survival while a newly identified Sensory-Pain subtype with a better prognosis. These data-driven phenotypes may improve prognostication and inform targeted supportive care.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

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Code availability

Analysis code is available at https://github.com/yabdulle/mnd-symptom-phenotyping

Reproduced under the paper's license (CC BY), from the paper cited above.

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Clinical data cannot be shared due to patient confidentiality.

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Version 1, 27 September 2026: the first record

Recorded: type, journal, dates, 12 authors, 8 keywords, 1 funder, 27 references.

Cite

This paper

Abdulle, Y., Dinu, V., Wu, J., Kim, Y., Budhdeo, S., Khelfiat, A. A., Yao, Z., Tomlinson, C., Al-Chalabi, A., Wu, H., Dobson, R., & Iacoangeli, A. (2026). Symptom-based phenotype discovery in motor neuron disease using natural language processing of electronic health records. medRxiv (preprint). https://doi.org/10.64898/2026.06.18.26355960

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