Headache Prediction Using Sensor Data and Machine Learning
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Solution Overview
Problem
Current treatments for migraines lack effectiveness and predictability, leading to inadequate management and potential transformation into chronic forms, with no proven bio-markers for prediction and subjective pain assessment methods that are biased and incomplete.
Innovation Solution
A computer-implemented method using machine learning models that predict headaches by feeding physiological, behavioral, and environmental parameters from sensors into a trained model, allowing for personalized predictions and adaptive treatment recommendations based on historical data and real-time sensor inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to predict headaches, then prediction accuracy and objectivity are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system segments the headache prediction problem into multiple independent components: data collection from various sensors, preprocessing of physiological/behavioral/environmental parameters, machine learning model processing, and treatment recommendation generation. This modular segmentation improves prediction accuracy while managing system complexity through divided responsibilities.
Solution Approach 2:
The patent introduces an intermediary processing layer between raw sensor data and prediction outcomes. This intermediary layer includes data preprocessing modules that clean, normalize, and feature-engineer the input data before feeding it to the machine learning model, thereby improving prediction accuracy without directly increasing the core model's complexity.
2Reliability
If multiple physiological, behavioral, and environmental parameters are collected from sensors, then prediction reliability is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and feature-engineering sensor data in advance, creating standardized data structures and extracting relevant features before the actual prediction is needed. This preliminary preparation improves prediction reliability by ensuring data quality while reducing processing time during critical prediction moments.
Solution Approach 2:
The patent extracts only the most relevant features from the extensive sensor data using feature selection techniques. By taking out and focusing on the most predictive parameters while discarding redundant information, the system maintains high prediction reliability while significantly reducing computational processing time and resource requirements.
3Measurement precision
If personalized training datasets are used for individual subjects, then prediction accuracy for that subject is improved, but data collection requirements and training time increase
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing historical sensor data for each subject in structured formats. This preliminary data collection and organization enables faster personalized model training when needed, as the raw data is already cleaned, labeled, and ready for processing, thereby improving personalized prediction accuracy without excessive training time delays.
Data Source
AI summary
There is provided a computer implemented method for predicting a headache for a subject, comprising: feeding a plurality of physiological, behavioral, and environmental parameters obtained from a plurality of sensors monitoring the subject, into a machine learning model, and obtaining the prediction of the headache for the subject as an outcome of the machine learning model, wherein the machine learning model is trained on a training dataset of a plurality of records of a plurality of sample individuals, wherein a record includes a plurality of sample physiological, behavioral, and environmental parameters of a sample individual and a ground truth label indicative of a state of a headache of the sample individual.