Parkinson's Disease

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The field studies how to detect Parkinson’s disease, measure symptom severity and progression, distinguish it from other conditions, and guide treatment. Common approaches analyze speech, gait, facial motion, typing, handwriting, clinical assessments, brain imaging, EEG, and molecular data using machine learning, self-supervised representations, and multimodal models. Researchers also investigate forecasting and personalized interventions, including diffusion models for progression and adaptive controllers for deep brain stimulation. A central challenge is proving that models generalize across people, clinics, languages, and devices, since site differences and data limitations can make strong benchmark results misleading.

Latest papers

36 papers1 paper in the last four weeks, down 80% on the four weeks before. 0.0% of all new papers.

PACE-RAG: Patient-Aware Contextual and Evidence-Constrained RAG for Clinical Drug Recommendation
Mar 18, 2026Chaeyoung Huh, Hyunmin Hwang, Jung Hwan Shin +3Retrieval-Augmented Generation Clinical Decision Support
1Korea Advanced Institute of Science and Technology, KAIST, Republic of Korea · Department of Neurology, Seoul National University Hospital, Republic of Korea · 3Asan Medical Center, University of Ulsan, College of Medicine, Republic of Korea +1
Evaluating Parkinson's Disease Detection in Anonymized Speech: A Performance and Acoustic Analysis
Mar 8, 2026Carlos Franzreb, Francisco Teixeira, Ben Luks +2Parkinson's Disease Detection from Speech Parkinson's Disease
German Research Center for Artificial Intelligence, Germany · INESC-ID, Portugal · Technical University of Berlin, Germany +1
Topological descriptors of foot clearance gait dynamics improve differential diagnosis of Parkinsonism
Mar 6, 2026Jhonathan Barrios, Wolfram Erlhagen, Miguel F. Gago +2Parkinson's Disease Classification Differential Diagnosis
Centre of Mathematics (CMAT), School of Sciences, University of Minho, Portugal · Neurology Departmet, Hospital da Senhora da Oliveira, Portugal · ICVS, School of Medicine, University of Minho, Portugal +3
Freezing of Gait Prediction using Proactive Agent that Learns from Selected Experience and DDQN Algorithm
Mar 4, 2026Septian Enggar Sukmana, Sang Won Bae, Tomohiro ShibataPrioritized Experience Replay Parkinson's Disease
Kyushu Institute of Technology · Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Japan · Stevens Institute of Technology +1
Detecting and explaining clinical-omics inconsistencies to improve patient cohort stratification: an application to Parkinson's disease
Jul 4, 2025José A. Pardo-Pérez, Tomás Bernal, Jaime Ñiguez +5Medical Diagnosis Unsupervised Anomaly Detection
Department of Information and Communications Engineering, Faculty of Computer Science, University of Murcia, Murcia, Spain. · Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK. · Department of Neurology, Washington University School of Medicine, Saint Louis, MO, 63110, USA. +1
Evidence for Phenotype-Driven Disparities in Freezing of Gait Detection and Approaches to Bias Mitigation
Jan 29, 2025Timothy Odonga, Jeanne M. Powell, Claire Bae +5Algorithmic Fairness Human Activity Recognition
Emory University · Georgia Institute of Technology

Mar 18, 2026J/K move · Enter open · S save

PACE-RAG: Patient-Aware Contextual and Evidence-Constrained RAG for Clinical Drug Recommendation

Chaeyoung Huh, Hyunmin Hwang, Jung Hwan Shin, Sungyang Jo+2

1Korea Advanced Institute of Science and Technology, KAIST, Republic of Korea · Department of Neurology, Seoul National University Hospital, Republic of Korea · 3Asan Medical Center, University of Ulsan, College of Medicine, Republic of Korea+1

Retrieval-Augmented Generation Clinical Decision Support Parkinson's Disease Medication Recommendation

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Drug recommendation requires a deep understanding of individual patient context, especially for complex conditions like Parkinson's disease. While LLMs possess broad medical knowledge, they fail to capture the subtle nuances of actual prescribing patterns. Existing RAG methods also struggle with these complexities because guideline-based retrieval remains too generic and similar-patient retrieval often replicates majority patterns without accounting for the unique clinical nuances of individual patients. To bridge this gap, we propose PACE-RAG (Patient-Aware Contextual and Evidence-Constrained RAG). Rather than directly copying frequent medications from retrieved patients, PACE-RAG personalizes recommendations by first extracting patient-specific clinical features, retrieving cases around these features, and then refining the final prescription using the patient's current symptoms, active medication history, and focus-specific prescribing tendencies. By analyzing treatment patterns tailored to specific clinical features, PACE-RAG generates patient-specific medication recommendations along with an explainable clinical summary. PACE-RAG achieved the strongest performance among the evaluated inference-only LLM-based methods, reaching F1 scores of 80.84% and 47.22% on the Parkinson's disease and MIMIC-IV cohorts, respectively. Our code is available at: https://github.com/ChaeYoungHuh/PACE-RAG.

Latest papers

PACE-RAG: Patient-Aware Contextual and Evidence-Constrained RAG for Clinical Drug Recommendation

Evaluating Parkinson's Disease Detection in Anonymized Speech: A Performance and Acoustic Analysis

Topological descriptors of foot clearance gait dynamics improve differential diagnosis of Parkinsonism

Freezing of Gait Prediction using Proactive Agent that Learns from Selected Experience and DDQN Algorithm

Detecting and explaining clinical-omics inconsistencies to improve patient cohort stratification: an application to Parkinson's disease

Evidence for Phenotype-Driven Disparities in Freezing of Gait Detection and Approaches to Bias Mitigation