Machine Learning Concussion Care Platform for Personalized Treatment
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Solution Overview
Problem
Current healthcare approaches for managing mild traumatic brain injury (mTBI) or concussion often rely on diagnosis with a 'hands off' treatment regimen of rest, failing to provide customized treatment plans, and healthcare providers are limited to assessing and treating only a few clinical domains.
Innovation Solution
A digital health platform utilizing machine learning models to analyze structured data from various clinical domains, including behavioral, cervicogenic, cognitive, and vestibular characteristics, to provide personalized treatment plans through a clinician web portal and patient mobile app, integrating with electronic medical records for comprehensive management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a 'hands off' treatment regimen of rest only is used, then the treatment approach is simple and easy to implement, but it fails to provide customized treatment plans and does not address multiple clinical domains
Solution Approach 1:
The system dynamically adapts treatment recommendations based on patient response and changing clinical conditions. The machine learning model continuously updates treatment plans as new data is collected from multiple clinical domains, transforming the static 'rest only' approach into a dynamic, responsive treatment regimen that evolves with patient needs.
Solution Approach 2:
The system changes treatment parameters based on analyzed clinical data across multiple domains. Instead of a fixed treatment protocol, the machine learning model adjusts treatment parameters (such as activity levels, therapeutic interventions, and recovery protocols) based on patterns identified in behavioral, cognitive, vestibular, and other clinical domain data.
2Productivity
If healthcare providers assess and treat only a few clinical domains, then the assessment process is simple and quick, but it limits the comprehensiveness of care and prevents personalized treatment insights
Solution Approach 1:
The system integrates multiple clinical domain assessments into a single unified platform that simultaneously evaluates behavioral, cognitive, vestibular, oculomotor, and other domains. This multi-functional approach allows healthcare providers to conduct comprehensive assessments across all relevant clinical domains without significantly increasing time requirements, as the system processes multiple data streams concurrently.
Solution Approach 2:
The system creates a comprehensive digital replica of the patient's clinical state by collecting and integrating data from multiple clinical domains. This digital copy contains complete clinical information that can be analyzed by the machine learning model to generate personalized treatment insights, preserving all relevant clinical information without requiring physical presence for each assessment type.
3Measurement precision
If machine learning models analyze structured data from multiple clinical domains, then personalized treatment insights are generated, but the system complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex analysis task by dividing it into distinct clinical domain modules (behavioral, cognitive, vestibular, oculomotor, etc.). Each module processes specific types of data independently before integrating results into the overall treatment recommendation. This segmentation manages complexity by organizing the system into manageable, specialized components while maintaining high prediction precision through comprehensive data analysis.
4Reliability
If comprehensive data collection from multiple sources is implemented, then complete patient profiles are created for better treatment personalization, but data integration and management become more difficult
Solution Approach 1:
The system employs an intermediary layer that standardizes and harmonizes data from multiple clinical domains and sources. This intermediary processing layer transforms diverse data formats and structures into a unified schema that the machine learning model can effectively analyze, simplifying data integration while maintaining complete and accurate patient profiles through systematic data normalization and validation.
Data Source
AI summary
In some aspects, the present disclosure provides a computer-implemented method for predicting a plurality of treatment options for treatment of a traumatic brain injury for a subject, the computer-implemented method comprising: (a) receiving a plurality of attributes of the subject, wherein the plurality of attributes is related to the traumatic brain injury; and (b) applying a machine learning model to the plurality of attributes to predict (i) a clinical outcome comprising a traumatic brain injury, and (ii) the plurality of treatment options for treatment of the traumatic brain injury.