Predictive Mental State Analysis Using Machine Learning Models
Solution Overview
Problem
Existing methods fail to predictively determine and manage factors affecting a user's mental state effectively, particularly in relation to nutraceutical usage, which can alter mood and mental health, posing challenges in safe and effective consumption.
Innovation Solution
A computer-implemented method using multiple machine learning models to analyze user data, events, and nutraceutical consumption patterns to classify mental states, predict future effects, and optimize dosage and timing to minimize negative impacts, incorporating models like ARIMA, LSTM, and regression models for anomaly detection and prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If nutraceuticals are used to alter mood and impact mental state, then mental health can be promoted, but safety and effectiveness are compromised due to lack of predictive control
Solution Approach 1:
The system performs preliminary analysis of user data including biometric information, environmental factors, and historical patterns before recommending nutraceutical usage. Machine learning models predict future mental state trajectories and identify optimal timing and dosages in advance, allowing users to take preventive action rather than reacting to mental state changes after they occur.
Solution Approach 2:
The system continuously monitors user biometric data and mental state indicators, comparing actual outcomes against predicted trajectories. This feedback loop allows the system to refine its predictions and adjust future recommendations, ensuring safe and effective nutraceutical usage through iterative learning from user responses.
2Measurement precision
If multiple machine learning models are used to predict mental state and optimize nutraceutical usage, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The prediction system is divided into specialized machine learning models, each handling specific aspects of mental state analysis. One model processes biometric data, another analyzes environmental factors, and a third predicts nutraceutical responses. This segmentation allows each model to be optimized for its specific function while working together to provide comprehensive predictions.
Solution Approach 2:
The machine learning models are designed to handle multiple types of input data and perform various prediction functions using the same underlying framework. The system can predict different aspects of mental state (mood, energy levels, cognitive function) and optimize multiple parameters (timing, dosage, duration) using a unified multi-functional approach.
Data Source
AI summary
There is provided a method and system for predictive determination of factors that afflict mental state of a user. The method includes receiving data associated with information that can afflict a mental state of the user; receiving data associated with events affecting the user; determining anomalies in a mental state of the user using a trained first machine learning model, the first machine learning model outputting a binary classifier indicating either that the user is in a normal mental state and or that the user is in an anomalous mental state; and determining a mental state of the user using a trained second machine learning model, the second machine learning model outputting a probability of the mental state of the user being either ordinary or extraordinary.