Symptom Forecasting Model for Neurodegenerative Disorder Monitoring
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Solution Overview
Problem
There is a need for improved techniques to accurately monitor and predict the intensity of symptoms associated with neurodegenerative disorders like Parkinson's disease, dyskinesia, and dystonia, to aid in effective treatment planning and medication management.
Innovation Solution
A computer-implemented method using sensor data from wearable devices, such as accelerometers and gyroscopes, to extract features and determine symptom-intensity scores. These scores are then used to generate predicted symptom-intensity scores through a symptom-forecasting model, incorporating medical information and mobility metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is collected and processed to monitor symptom intensity, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the monitoring function into separate components: wearable sensors for data collection, cloud-based processing for feature extraction, and visualization interfaces for display. This divides the complex task of symptom monitoring into manageable modules that can be implemented and maintained more easily.
Solution Approach 2:
A cloud-based processing system acts as an intermediary between the simple wearable sensors and the complex analysis requirements. The cloud platform handles feature extraction, pattern recognition, and prediction algorithms, allowing the wearable device to remain simple while achieving sophisticated monitoring capabilities.
2Productivity
If feature extraction and forecasting models are implemented, then productivity is improved through timely interventions, but loss of time increases due to data processing requirements
Solution Approach 1:
The system performs preliminary feature extraction and pattern learning during offline processing phases, where historical data is analyzed to build forecasting models. During real-time operation, the system only needs to extract basic features and query pre-trained models, significantly reducing processing time while maintaining high productivity through timely symptom predictions.
Data Source
AI summary
The present disclosure relates to a method and system for acquiring and analyzing multi-modal data to monitor, forecast, and manage one or more symptoms of the neurodegenerative disorders such as Parkinson disease of a subject. The multi-modal data may include sensor data from a wearable sensing device, medications data, symptom-intensity scores for one or more symptoms, and mobility metrics of the subject. A predicted symptom-intensity score (e.g., absolute or relative value) may be generated for each of the one or more symptoms using a symptom-forecasting model (e.g., a machine learning model) and for each of one or more future time periods. Based on the predicted symptom-intensity scores, a trend can be generated for a selected time period. The disclosed system may output a result that comprises of intervention actions, such as medication administration, physical activity, or any other intervention that can be used to control symptoms severity.