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LLM-Guided Adaptive Cost Calibration in Active Learning for Parkinson’s Disease Biosignals

Year: 2026
Type: article
Abstract: Confirmatory labels for Parkinson’s disease (PD), from expert examination or, prospectively, biological assays, are costly, and missed cases and false alarms carry unequal consequences. ClinRisk-LLM is a framework for label-efficient, cost-aware learning from gait and speech signals in which a large language model (LLM) translates a predefined safety specification into a frozen policy setting the positive-class weight of a support vector machine and the priority of potential missed cases for annotation. Cost-sensitive training, risk-aware querying and the LLM policy were ablated on gait (n=165) and speech (n=252) data and benchmarked against post hoc thresholding, a refer-all policy, held-out gait studies and an independent speech cohort. With half of the labels, cost-sensitive active learning reduced the 4:1 asymmetric cost by 34.1% (gait) and 5.7% (speech) relative to standard active learning, at the expense of specificity. ClinRisk-LLM translated the predefined safety specification into a frozen, auditable policy that adjusted risk settings during label acquisition. Component-wise analyses indicated that classifier weighting accounted for most of the observed cost reduction, while the incremental benefit of LLM-guided adaptation varied across datasets and comparison settings. A threshold-tuned symmetric model came within 1.3 cost points for gait, and gait remained roughly level with referring everyone. ClinRisk-LLM reached a gait cost of 47.60 versus 46.10 for fixed cost-sensitive learning; 40 policies generated across three LLMs from a reconstructed specification yielded gait costs of 52.1–75.8. Applications to molecular triage remain prospective. Code and data availability details are provided in the Data Availability Statement.
DOI: 10.3390/ijms27219542
Institution: Atlas Üniversitesi
Language: English
Open Access status: gold
Estimated APC paid: $3,499
OpenAlex ID: W7223960568
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