Interpretable NLP-Based Analysis of Longitudinal Electronic Health Records for Early Prediction of Parkinson’s Disease: A Retrospective Multicenter Study in India
Year: 2026
Type: article
Study design: Observational Study
Abstract: Parkinson’s disease (PD) is often diagnosed after substantial delay in India, limiting timely intervention. Unstructured longitudinal electronic health records (EHRs) may contain prodromal signals, but NLP-based approaches are understudied in Indian settings.We conducted a retrospective multicenter study using de-identified EHRs from AIIMS New Delhi and PGIMER Chandigarh (2015–2025). From 45,000 documents across 15,000 patients with neurological symptoms, 2,500 had confirmed PD. After exclusions and NLP pre-filtering, the final cohort comprised 8,500 patients (1,800 PD-positive, 6,700 controls). PD was defined by MDS clinical criteria; DaTSCAN supported 78% of diagnoses. Mimics were excluded. We developed an NLP-XGBoost pipeline using BioBERT for named entity recognition, negation detection, and temporal BiLSTM embeddings aggregated via attention pooling. A temporal split prevented leakage; a 6-month pre diagnosis washout excluded diagnostic workup notes. SHAP provided interpretability. The model achieved AUC 0.923 (95% CI 0.908 0.937), sensitivity 89.4%, specificity 91.2%, PPV 85.6%, NPV 93.1%, and F1-score 0.874, outperforming logistic regression (AUC 0.847) and CNN (AUC 0.891). Calibration was good (Brier 0.082; slope 1.02), PR-AUC was 0.891, and NRI was 0.32. External validation at SGPGI Lucknow yielded AUC 0.897. PD risk was predicted 2–5 years before diagnosis, with performance across horizons (AUC 0.941 at 6 months to 0.898 at 5 years). SHAP identified slow turning (mean |SHAP| 0.42), speech dysfluency (0.31), and REM sleep complaints (0.28) as top predictors. Rural patients showed bradykinesia signals 18 months earlier. Among 1,240 high-risk patients, 68% converted within 4 years (HR 12.4). Retrospective modeling projected a 41% reduction in diagnostic delay. Interpretable NLP of Indian EHRs offers a scalable, imaging-free prescreening approach with potential to support earlier referral. Prospective validation is warranted to confirm clinical impact.
Source: O Mundo da Saúde
Authors: Madhu Yadav, Pushpendra Kumar Verma
Institution: IIMT University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7224662615