Early Detection of Cognitive Decline in Parkinson's Disease Using Natural Language Processing of Clinical Notes: A Systematic Review and Meta-Analysis Protocol

Background: Cognitive decline affects approximately 40% of Parkinson's disease (PD) patients within 10 years of diagnosis, progressing to dementia in 80% of patients after 20 years. Early detection of cognitive changes is crucial for timely intervention and care planning. Clinical notes contain rich, longitudinal information about cognitive symptoms that may precede formal diagnosis, yet this unstructured data remains largely unexplored for systematic cognitive decline detection. Objective: To systematically review and meta-analyze studies using natural language processing (NLP) techniques to detect early cognitive decline in Parkinson's disease patients from clinical notes, evaluating the accuracy, timing, and predictive features of different NLP approaches. Methods: This systematic review will follow PRISMA guidelines and search PubMed, Embase, Web of Science, IEEE Xplore, ACL Anthology, and PsycINFO from inception to December 2025. Eligible studies must apply NLP methods to clinical notes for detecting or predicting cognitive decline in PD patients. Two reviewers will independently screen studies using Covidence software. Quality assessment will use PROBAST+AI for prediction model studies and QUADAS-2 for diagnostic accuracy studies. Meta-analysis will pool diagnostic accuracy measures using bivariate random-effects models where appropriate. Data Synthesis: Narrative synthesis will describe NLP methodologies, feature extraction techniques, and validation approaches. Meta-analysis will calculate pooled sensitivity, specificity, and area under the curve (AUC) for studies with comparable outcomes. Subgroup analyses will examine performance by NLP approach (rule-based vs. machine learning vs. deep learning), cognitive domain, and time to diagnosis. Discussion: This review will establish the current evidence base for NLP-based early detection of cognitive decline in PD, identify optimal methodological approaches, and guide future development of clinical decision support tools for cognitive monitoring in PD populations.