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Abstract

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.

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