Global Framework
Welcome to WWAL's global framework—a designed system of principles and agreements crafted to address pressing international issues across diverse domains. The framework serves as the cornerstone of the company's mission, fostering collaboration and cooperation among others to tackle common challenges and promote collective prosperity.
Conduct immersive sessions to uncover compelling stories from healthcare stakeholders. Use these narratives as a foundation for developing personalized care algorithms.
Discovery Sessions
Build a team of machine learning experts, healthcare professionals, and UX designers to collaborate on harmonizing algorithms into a cohesive system for personalized care plans.
Collaborative AI Team
Adhere to HL7 standards for interoperability and implement SMART on FHIR to seamlessly integrate apps into EHR systems, creating a cohesive healthcare ecosystem.
Interoperability Standards
Utilize Apache Kafka for real-time data streaming and Apache Flink for low-latency, high-throughput stream processing.
Real-time Data Processing:
Utilize AWS Lambda for serverless computing and Kubernetes clusters on Google Cloud Platform for scalable management of containerized applications.
Scalable Cloud Infrastructure
Develop interactive Jupyter notebooks enhanced with LMS (Learning Management System) APIs to seamlessly integrate AI and machine learning training processes, facilitating an intuitive and efficient learning experience for users.
Education and Training Platforms
Conduct immersive sessions to uncover compelling stories from healthcare stakeholders. Use these narratives as a foundation for developing personalized care algorithms.
Holistic Data Fusion
Simulate real-world healthcare challenges for model validation, ensuring algorithms navigate through scenarios that mirror the complexity of healthcare decision-making.
Real-World Model Validation
Develop responsive and interactive user interfaces using React or Angular, and implement D3.js for dynamic data visualization and informative dashboards.
User Interface Development
Deploy machine learning models as microservices with Docker containers and implement GraphQL for flexible APIs tailored to decision support systems' evolving requirements.
AI-driven Decision Support Systems
Implement Prometheus for monitoring metrics and alerts, and utilize A/B testing frameworks for continuous optimization of machine learning models.
Continuous Monitoring and Improvement:
Implement chatbots with NLP for real-time community interaction and utilize social network analysis to gauge and respond to community sentiments and feedback.
Community Engagement Platforms