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Explainable machine learning identifies predictors for postural instability and cognitive impairment in early Parkinson’s Disease
Sailer B, Roeben B, Kiefer C, Achauer L, Kemmner R, Wurster I, Lerche S, Bender B et al.. medRxiv, 2026-10-05
Topic: Brain imaging, connectivity and assessment
Parkinson’s disease (PD) exhibits considerable variability in symptom onset, severity, and clinical trajectories, making prediction of individual disease progression a complex challenge. Leveraging large multimodal datasets from two independent cohorts of early PD patients, we examined predictive features for progression to mild cognitive impairment (MCI) and postural instability (PI) using Explainable Boosting Machines (EBM) - an interpretable machine learning (ML) approach. We found comparable predictive performances of EBM models using a minimal clinico-demographic dataset compared to an extended multimodal dataset including volumetric brain imaging and fluid biomarkers. Baseline age and Montréal Cognitive Assessment total score were identified as most important predictive features for both MCI and PI using the minimal dataset, while additional motor, cognitive and imaging features were identified using the extended dataset. Our study highlights the potential of ML predictions of cognitive and motor endpoints of PD progression at the individual level bringing personalized clinical decision-making within reach.
Key findings
- We found comparable predictive performances of EBM models using a minimal clinico-demographic dataset compared to an extended multimodal dataset including volumetric brain imaging and fluid biomarkers.
Study details
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 21 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 13 of the 62 points we could judge it on, which is 21 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (medRxiv) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 13 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 13 of the 62 points we could judge it on is 21 out of 100. |
| Total | 21 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
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Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Sailer, B., Roeben, B., Kiefer, C., Achauer, L., Kemmner, R., Wurster, I., et al. (2026). Explainable machine learning identifies predictors for postural instability and cognitive impairment in early Parkinson’s Disease. medRxiv. https://doi.org/10.64898/2026.09.30.26364120
Vancouver
Sailer B, Roeben B, Kiefer C, Achauer L, Kemmner R, Wurster I, et al. Explainable machine learning identifies predictors for postural instability and cognitive impairment in early Parkinson’s Disease. medRxiv. 2026. doi:10.64898/2026.09.30.26364120.
BibTeX
@article{sailer2026-b5a66a6c,
author = {Sailer, B. and Roeben, B. and Kiefer, C. and Achauer, L. and Kemmner, R. and Wurster, I. and Lerche, S. and Bender, B. and others},
title = {{Explainable machine learning identifies predictors for postural instability and cognitive impairment in early Parkinson’s Disease}},
journal = {medRxiv},
year = {2026},
doi = {10.64898/2026.09.30.26364120},
url = {https://doi.org/10.64898/2026.09.30.26364120}
}