Problem
In today’s healthcare landscape, early detection and prevention of diseases remain critical, yet traditional methods of risk assessment are often limited in scope, focusing primarily on isolated factors like family history or basic demographic data. These methods typically lack the depth and precision needed to address the complex interplay of genomics, lifestyle, and environmental factors that contribute to disease risk. As a result, healthcare providers and individuals are left with incomplete risk profiles, leading to missed opportunities for proactive intervention and personalized care. This gap creates a pressing need for a more comprehensive, data-driven approach to health risk assessment.
Solution
IntelliGenes addresses this gap by offering a sophisticated, AI-driven platform that integrates genomic, demographic, lifestyle, and environmental data to provide a highly personalized and predictive health risk profile. The core of IntelliGenes’ innovation is its proprietary “I-Score,” which aggregates diverse data points to offer a comprehensive disease risk assessment for each individual. This actionable score enables healthcare providers to identify high-risk individuals earlier, personalize treatment plans, and recommend targeted lifestyle changes. Additionally, IntelliGenes empowers users to make informed health decisions by visualizing their health risks and providing data-driven, personalized health insights and recommendations.
By combining cutting-edge machine learning with advanced data integration techniques, IntelliGenes moves beyond traditional risk assessment to deliver a dynamic, holistic view of each individual’s health profile, transforming disease prevention and enabling a proactive approach to personalized healthcare.
Tools Used
Project Overview
IntelliGenes is an AI-powered healthcare platform that integrates genomic, demographic, lifestyle, and environmental data to provide personalized health risk assessments. The platform’s core feature, the "I-Score," empowers healthcare providers and users to make proactive health decisions based on predictive insights.
My Role
I played a key role in data analysis, model development, and user experience design for IntelliGenes. Working closely with data scientists and healthcare experts, I helped refine the machine learning model for calculating the I-Score and developed UI mockups to ensure a user-friendly experience.
Data Collection and Analysis
Data collection involved gathering information from various sources, including public health records, wearable devices, and genomic databases.
Data Cleaning: Removing irrelevant or inconsistent entries to improve data integrity.
Feature Engineering: Transforming raw genomic and demographic data into structured features that enhance model predictiveness.
Statistical Testing: Validating the significance of different health indicators to ensure their relevance in predicting disease risks.
Key Recommendations
Increase User Engagement: Add regular health insights and personalized suggestions.
Expand Data Integration: Incorporate data from additional sources like fitness apps.
Continuous Improvement: Implement a feedback loop to enhance I-Score accuracy and usability.
Privacy Enhancements: Strengthen user control over data sharing.