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AI-Powered Parkinson's Disease Progression Prediction Platform

dac.digital

Health & Fitness

Medical

Information technology

Challenge

Parkinson's Disease (PD) progression is highly variable, making personalized treatment difficult. Current methods for monitoring progression (medical history, physical exams, rating scales) lack predictive accuracy. GNRI seeks to develop a more accurate, data-driven method for predicting PD progression to enable earlier interventions and improve patient quality of life. Existing data is often incomplete and requires sophisticated processing.

About the Client

GNRI is a leading non-profit research institute dedicated to advancing the understanding and treatment of neurological disorders, with a focus on Parkinson's Disease. They collaborate with hospitals, clinics, and pharmaceutical companies to accelerate research and improve patient outcomes.

Objectives

Develop a machine learning model to predict MDSUPDRS scores for individual PD patients with a specified time horizon (e.g., 6 months, 12 months).
Identify key biomarkers (protein/peptide levels, clinical factors, visit frequency) that significantly influence PD progression.
Create a user-friendly platform for clinicians to visualize predicted progression trajectories and associated risk factors.
Improve the accuracy and reliability of PD progression prediction compared to existing methods.
Enhance GNRI's research capabilities in applying advanced analytics to complex neurological data.

Functionality

Data ingestion and preprocessing (handling missing data, normalization).
Feature engineering and selection.
Machine learning model training and evaluation (CatBoost, XGBoost, TabNet).
Personalized prediction generation (MDSUPDRS score prediction).
Visualization of predicted progression trajectories.
Identification of key predictive biomarkers.
Clinician dashboard for data review and prediction interpretation.
Model retraining and monitoring capabilities.

Technology Stack

Python

Pandas

CatBoost

XGBoost

TabNet

Optuna

Cloud-based platform (AWS, Azure, GCP)

Integrations

Electronic Health Record (EHR) systems (e.g., Epic, Cerner) for data retrieval
Laboratory information systems (LIS) for accessing biomarker data

Non-Functional Requirements

High accuracy and reliability of prediction models.
Scalability to handle large datasets.
Data security and privacy (HIPAA compliance).
Performance for real-time prediction generation.
User-friendly interface for clinicians.

Impact

Successful implementation of this platform will enable GNRI and collaborating institutions to provide more personalized and proactive care for PD patients. Earlier identification of high-risk individuals will facilitate timely interventions, potentially slowing disease progression and improving patient outcomes. The platform will also accelerate research by providing valuable insights into the complex factors driving PD progression, leading to the development of more effective treatments.