LifeSignals

LifeSignals by Techlife Collective. An agentic federated learning environment

Inspiration

LifeSignals by Techlife Collective was born out of personal experience and a pressing need in mental health care. After losing my niece to mental health challenges and supporting my grandmother through dementia, I witnessed firsthand the immense emotional and physical toll on families. In countries with limited data on neurocognitive and neuropsychiatric disorders, research into causes, preventive measures, and potential treatments is severely constrained.

This platform aims to empower palliative caregivers, providing them with actionable, on-demand guidance to manage complex care scenarios while building a secure, local dataset that advances research and improves care practices.

What it does

LifeSignals is an agent-driven federated learning platform for palliative caregivers managing neurocognitive and neuropsychiatric disorders. It:

The platform's AI agent prepare, orchestrate and manage a federated learning environment, automating the hard tasks of a data scientist and software engineer.
Provides real-time, role-specific guidance for caregivers, community health workers, clinicians, and students learning the professionals.
Orchestrates AI-assisted decision support while maintaining patient privacy using secure aggregation and differential privacy.
Enables local model training and inference, minimizing PHI transfer and empowering clinics with low-latency insights.
Collects and curates a secure, consent-aware dataset to inform research and improve patient outcomes in LMICs and regions with low local datasets.

How we built it

Our project was built through a series of carefully coordinated steps, combining healthcare data systems, AI, and user-facing applications. Here's the process we followed:

EHR Development
We started by designing and implementing the Electronic Health Record (EHR) system to securely store and manage patient health data. This involved creating database schemas, APIs, and interfaces that ensure data privacy and accessibility for authorized users.
Federated Learning (FL) Model Development
Parallel to the EHR, we developed our Federated Learning models to enable privacy-preserving AI training across distributed datasets. This allowed us to leverage multiple data sources without centralizing sensitive patient information.
EHR and FL Model Integration
Once both systems were operational, we integrated the EHR with the FL models. This ensured the models could access relevant health data securely, enabling accurate predictions and recommendations while maintaining compliance with privacy standards.
Frontend Development
The user interface was built with a focus on usability and accessibility. We designed dashboards and interactive components that allow users to view and manage health data, as well as interact with AI-driven insights.
Backend Development and Frontend Integration
The backend APIs were developed to connect the frontend with the database and AI services. We ensured smooth communication between the layers, handling authentication, data validation, and secure data transmission.
AI Model Development and Backend Integration
We built advanced AI models capable of analyzing health data, generating insights, and providing actionable recommendations. These models were integrated with the backend, allowing real-time inference based on user data.
AI Models and Frontend Integration
The AI capabilities were then connected to the frontend, giving users direct access to model predictions, recommendations, and analytics in an intuitive interface.
Testing and Validation
Finally, we conducted thorough testing at every stage — unit tests, integration tests, and system-wide testing — to ensure reliability, accuracy, and performance. This included testing the AI models, data workflows, and user interactions to deliver a robust final product.

This structured approach allowed us to build a cohesive system where EHR, AI models, and user interfaces work seamlessly together, ensuring both utility and privacy for end-users.

Challenges we ran into

Ensuring data privacy and compliance while enabling collaborative model learning.
Implementing secure aggregation and differential privacy at scale.
Balancing emergency automation with human oversight to ensure safety.
Coordinating federated learning across heterogeneous edge nodes in low-resource clinic environments.
AWS Services went offline last minute in east-us location where my main agent was residing having to build new ones quickly
The unusually and unexpected AWS service outage made me deviate course then learnt last minute my new approach wouldn't work which limited my development time

Accomplishments that we're proud of

Built a fully agentic platform for privacy-preserving federated learning.
Enabled real-time guidance for caregivers in sensitive clinical contexts.
Successfully integrated edge deployment for low-latency, local inference.
Established audit, consent, and explainability pipelines, meeting high governance standards.
Developed a framework for securely building a local research dataset, empowering future studies.

What we learned

Federated learning in healthcare requires careful orchestration between edge nodes and the cloud.
Consent and privacy must be embedded at every layer of design, not as an afterthought.
AI agents can augment caregiver decision-making, but human oversight remains critical.
Building locally relevant datasets is key to addressing research gaps in underrepresented populations.

What's next for LifeSignals by Techlife

Expand deployment to clinics and community health centers.
Enhance model explainability and RAG capabilities to provide richer guidance.
Integrate additional modalities, such as wearable data and EHR streams, for more comprehensive insights.
Use the securely aggregated datasets to drive research on neurocognitive and neuropsychiatric disorders.
Continue refining emergency automation workflows and support tools for palliative caregivers.